1
|
Ma Y, Yue P, Zhang J, Yuan J, Liu Z, Chen Z, Zhang H, Zhang C, Zhang Y, Dong C, Lin Y, Liu Y, Li S, Meng W. Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform. Ann Med 2024; 56:2357354. [PMID: 38813815 PMCID: PMC11141304 DOI: 10.1080/07853890.2024.2357354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 04/26/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity. METHODS We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961). RESULTS A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757-0.926) and 0.914 (0.851-0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility. CONCLUSIONS We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.
Collapse
Affiliation(s)
- Yuhu Ma
- Department of Anesthesiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Ping Yue
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Jinduo Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Jinqiu Yuan
- Clinical Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zhaoqing Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zixian Chen
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hengwei Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Chao Zhang
- Department of Orthopedics, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yong Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Chunlu Dong
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yanyan Lin
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yatao Liu
- Department of Anesthesiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wenbo Meng
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| |
Collapse
|
2
|
Fu FX, Cai QL, Li G, Wu XJ, Hong L, Chen WS. The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression. Magn Reson Imaging 2024; 111:168-178. [PMID: 38729227 DOI: 10.1016/j.mri.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/24/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024]
Abstract
OBJECTIVE The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner. METHODS A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy. RESULTS Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93-1). CONCLUSION The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.
Collapse
Affiliation(s)
- Fang-Xiong Fu
- Department of Radiology, Shenzhen Longhua District Central Hospital, Shenzhen 518110, China
| | - Qin-Lei Cai
- Department of Radiology, Hainan General Hospital, Haikou 570311, China
| | - Guo Li
- Department of Radiology, Hainan General Hospital, Haikou 570311, China
| | - Xiao-Jing Wu
- Department of Radiology, Hainan General Hospital, Haikou 570311, China
| | - Lan Hong
- Department of Gynecology, Hainan General Hospital, Haikou 570311, China.
| | - Wang-Sheng Chen
- Department of Radiology, Hainan General Hospital, Haikou 570311, China.
| |
Collapse
|
3
|
Huang Z, Mo S, Wu H, Kong Y, Luo H, Li G, Zheng J, Tian H, Tang S, Chen Z, Wang Y, Xu J, Zhou L, Dong F. Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics. PHOTOACOUSTICS 2024; 38:100606. [PMID: 38665366 PMCID: PMC11044033 DOI: 10.1016/j.pacs.2024.100606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/26/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
Background The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. Purpose This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and methods From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3-5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. Results The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules. Conclusion This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.
Collapse
Affiliation(s)
- Zhibin Huang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Sijie Mo
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Huaiyu Wu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Yao Kong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Hui Luo
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Guoqiu Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Jing Zheng
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Shuzhen Tang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Zhijie Chen
- Ultrasound Imaging System Development Department, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China
| | - Youping Wang
- Department of Clinical and Research, Shenzhen Mindray Bio-medical Electronics Co., Ltd., Shenzhen, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Luyao Zhou
- Department of Ultrasound, Shenzhen Children’ Hospital, No. 7019, Yitian Road, Futian District, Shenzhen 518026, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| |
Collapse
|
4
|
Lv X, Lu JJ, Song SM, Hou YR, Hu YJ, Yan Y, Yu T, Ye DM. Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study. Clin Otolaryngol 2024; 49:462-474. [PMID: 38622816 DOI: 10.1111/coa.14162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/28/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
INTRODUCTION To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). METHODS A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. RESULTS Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. CONCLUSION We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.
Collapse
Affiliation(s)
- Xin Lv
- Department of Oncology, Yingkou Central Hospital, Yingkou, People's Republic of China
| | - Jing-Jing Lu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Si-Meng Song
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yi-Ru Hou
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yan-Jun Hu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Yan Yan
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| | - Dong-Man Ye
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China
| |
Collapse
|
5
|
Bestetti A, Zangheri B, Gabanelli SV, Parini V, Fornara C. Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis. Nucl Med Commun 2024; 45:642-649. [PMID: 38632972 PMCID: PMC11149941 DOI: 10.1097/mnm.0000000000001853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls. METHODS 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other. RESULTS Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects. CONCLUSION The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.
Collapse
Affiliation(s)
- Alberto Bestetti
- Department of Clinical and Community Sciences, State University of Milan, Milan
- Nuclear Medicine Department, MultiMedica Hospital
| | | | | | | | - Carla Fornara
- Division of Neurology, MultiMedica Hospital, Sesto San Giovanni, Italy
| |
Collapse
|
6
|
Zhang C, Ma LD, Zhang XL, Lei C, Yuan SS, Li JP, Geng ZJ, Li XM, Quan XY, Zheng C, Geng YY, Zhang J, Zheng QL, Hou J, Xie SY, Lu LH, Xie CM. Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma. J Magn Reson Imaging 2024; 60:231-242. [PMID: 37888871 DOI: 10.1002/jmri.29064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE Retrospective. POPULATION 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Cheng Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-di Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Cai Lei
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha-Sha Yuan
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jian-Peng Li
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Zhi-Jun Geng
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xin-Ming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xian-Yue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chao Zheng
- Shukun (Beijing) Technology Co, Ltd., Beijing, China
| | - Ya-Yuan Geng
- Shukun (Beijing) Technology Co, Ltd., Beijing, China
| | - Jie Zhang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Qiao-Li Zheng
- Department of Pathology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Jing Hou
- Department of Radiology, Hunan Cancer Hospital, Guangzhou, China
| | - Shu-Yi Xie
- Department of Radiology, Guangzhou People's Eighth Hospital, Guangzhou, China
| | - Liang-He Lu
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuan-Miao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| |
Collapse
|
7
|
Scicolone R, Vacca S, Pisu F, Benson JC, Nardi V, Lanzino G, Suri JS, Saba L. Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque. Eur J Radiol 2024; 176:111497. [PMID: 38749095 DOI: 10.1016/j.ejrad.2024.111497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/14/2024] [Accepted: 05/03/2024] [Indexed: 06/17/2024]
Abstract
Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.
Collapse
Affiliation(s)
- Roberta Scicolone
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy
| | - Sebastiano Vacca
- University of Cagliari, School of Medicine and Surgery, Cagliari, Italy
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy
| | - John C Benson
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Valentina Nardi
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero-Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, Cagliari, Italy.
| |
Collapse
|
8
|
Li W, Liu J, Xiao Z, Zhu D, Liao J, Yu W, Feng J, Qian B, Fang Y, Li S. Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images. Insights Imaging 2024; 15:143. [PMID: 38867121 PMCID: PMC11169124 DOI: 10.1186/s13244-024-01719-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/21/2024] [Indexed: 06/14/2024] Open
Abstract
OBJECTIVES To establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model's effectiveness. MATERIALS AND METHODS Plain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren-Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC). RESULTS The AUC (macro/micro) of the model using a combination of AP and LAT (AP&LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p < 0.05). The overall accuracy of the radiomic model with AP&LAT images was 0.727 compared to 0.712 and 0.417 for radiologists with 4 years and 2 years of musculoskeletal diagnostic experience. CONCLUSIONS A radiomic model constructed by combining the AP&LAT images of the knee joint can better grade knee OA and assist clinicians in accurate diagnosis and treatment. CRITICAL RELEVANCE STATEMENT A radiomic model based on plain radiographs accurately grades knee OA severity. By utilizing the LR classifier and combining AP&LAT images, it improves accuracy and consistency in grading, aiding clinical decision-making, and treatment planning. KEY POINTS Radiomic model performed more accurately in K/L grading of knee OA than junior radiologists. Radiomic features from the combined images achieved better classification performance than the individual position image. A radiomic model can improve the grading of knee OA and assist in diagnosis and treatment.
Collapse
Affiliation(s)
- Wei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Jin Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Zhongli Xiao
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Dantian Zhu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Jianwei Liao
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Wenjun Yu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Jiaxin Feng
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Baoxin Qian
- Huiying Medical Technology (Beijing), Huiying Medical Technology Co., Ltd., Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, 100192, China
| | - Yijie Fang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China
| | - Shaolin Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, China.
| |
Collapse
|
9
|
Yao W, Wang Y, Zhao X, He M, Wang Q, Liu H, Zhao J. Automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning. Medicine (Baltimore) 2024; 103:e38503. [PMID: 38847664 PMCID: PMC11155539 DOI: 10.1097/md.0000000000038503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/17/2024] [Indexed: 06/10/2024] Open
Abstract
The aim of this study was to construct a classification model for the automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning. We retrospectively collected elbow joint Radiographs of children aged 3 to 14 years and manually delineated regions of interest (ROI) using ITK-SNAP. Radiomics features were extracted using pyradiomics, a python-based feature extraction tool. T-tests and the least absolute shrinkage and selection operator (LASSO) algorithm were used to further select the most valuable radiomics features. A logistic regression (LR) model was trained, with an 8:2 split into training and testing sets, and 5-fold cross-validation was performed on the training set. The diagnostic performance of the model was evaluated using receiver operating characteristic curves (ROC) on the testing set. A total of 411 fracture samples and 190 normal samples were included. 1561 features were extracted from each ROI. After dimensionality reduction screening, 40 and 94 features with the most diagnostic value were selected for further classification modeling in anteroposterior and lateral elbow radiographs. The area under the curve (AUC) of anteroposterior and lateral elbow radiographs is 0.65 and 0.72. Radiomics can extract and select the most valuable features from a large number of image features. Supervised machine-learning models built using these features can be used for the diagnosis of pediatric supracondylar humerus fractures.
Collapse
Affiliation(s)
- Wuyi Yao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| | - Yu Wang
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| | - Xiaobin Zhao
- Department of Radiology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| | - Man He
- Department of Rehabilitation, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| | - Qian Wang
- Department of Orthopedics, Tianjin Beichen Hospital, Tianjin, PR China
| | - Hanjie Liu
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| | - Jingxin Zhao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China
| |
Collapse
|
10
|
Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
Collapse
Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| |
Collapse
|
11
|
Tian R, Duan X, Xing F, Zhao Y, Liu C, Li H, Kong N, Cao R, Guan H, Li Y, Li X, Zhang J, Wang K, Yang P, Wang C. Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03192-1. [PMID: 38836956 DOI: 10.1007/s11548-024-03192-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA. MATERIALS AND METHODS After careful selection, data from 142 patients were randomly divided into a training set (n = 99) and a test set (n = 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P < 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis. RESULTS In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681-0.906), the radiomic model was 0.854 (95% CI 0.743-0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804-0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814-1.000), the radiomic model was 0.709 (95% CI 0.541-0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842-1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value. CONCLUSION We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.
Collapse
Affiliation(s)
- Run Tian
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xudong Duan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Fangze Xing
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiwei Zhao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - ChengYan Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Heng Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ning Kong
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ruomu Cao
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Huanshuai Guan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yiyang Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinghua Li
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jiewen Zhang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Kunzheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Pei Yang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Chunsheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| |
Collapse
|
12
|
Feng J, Hui D, Zheng Q, Guo Y, Xia Y, Shi F, Zhou Q, Yu F, He X, Wang S, Li C. Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI. Comput Biol Med 2024; 178:108684. [PMID: 38852399 DOI: 10.1016/j.compbiomed.2024.108684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. MATERIALS AND METHODS A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. RESULTS Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. CONCLUSION The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.
Collapse
Affiliation(s)
- Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China; Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongming Hui
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Qingqing Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Yi Guo
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shike Wang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
| |
Collapse
|
13
|
Collarino A, Feudo V, Pasciuto T, Florit A, Pfaehler E, de Summa M, Bizzarri N, Annunziata S, Zannoni GF, de Geus-Oei LF, Ferrandina G, Gambacorta MA, Scambia G, Boellaard R, Sala E, Rufini V, van Velden FH. Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer? J Nucl Med 2024; 65:962-970. [PMID: 38548352 DOI: 10.2967/jnumed.123.267044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/15/2024] [Indexed: 06/05/2024] Open
Abstract
This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.
Collapse
Affiliation(s)
- Angela Collarino
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vanessa Feudo
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Tina Pasciuto
- Research Core Facility Data Collection G-STeP, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Anita Florit
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Elisabeth Pfaehler
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Marco de Summa
- PET/CT Center, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Nicolò Bizzarri
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Salvatore Annunziata
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Gian Franco Zannoni
- Gynecopathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Pathology, Department of Woman and Child Health and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands
- Department of Radiation Science and Technology, Technical University of Delft, Delft, The Netherlands
| | - Gabriella Ferrandina
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Maria Antonietta Gambacorta
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Roma, Italy
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands; and
| | - Evis Sala
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
- Advanced Radiodiagnostics Centre, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vittoria Rufini
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy;
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Floris Hp van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|
14
|
Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga AI, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer HP, Schlett CL, Maier-Hein K, Bamberg F. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics. Insights Imaging 2024; 15:124. [PMID: 38825600 PMCID: PMC11144687 DOI: 10.1186/s13244-024-01704-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/20/2024] [Indexed: 06/04/2024] Open
Abstract
OBJECTIVES Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
Collapse
Affiliation(s)
- Ralf Floca
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany.
| | - Jonas Bohn
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Faculty of Bioscience, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christian Haux
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TU Munich University Hospital, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, TU Munich, Munich, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Annika Reinke
- Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Weiß
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Nolden
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Albert
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Tobias Norajitra
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Marc Dewey
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin Institute of Health, DZHK (German Centre for Cardiovascular Research), and DKTK (German Cancer Consortium), both partner sites Berlin, Berlin, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
- Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
| | - Martin Büchert
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Eva Maria Fallenberg
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich (FZJ), Juelich, Germany
- Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Bonn, Cologne & Duesseldorf, Germany
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Faculty 3, Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Tobias Haueise
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Marco Janoschke
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Matthias Jung
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Lena Sophie Kiefer
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jürgen Machann
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | | | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Barbara D Wichtmann
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Wenzhao Zhao
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heinz-Peter Schlemmer
- German Cancer Research Center (DKFZ) Heidelberg, Division of Radiology, Heidelberg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| |
Collapse
|
15
|
Dragon JM, Guha S, Salvatore MM. Hounsfield units: Future applications in clinical practice, radiomics, and Artificial Intelligence. Clin Imaging 2024; 110:110141. [PMID: 38608412 DOI: 10.1016/j.clinimag.2024.110141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/21/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Affiliation(s)
- Jacqueline M Dragon
- Department of Radiology, Columbia University Vagelos College of Physicians and Surgeons, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, United States of America
| | - Siddharth Guha
- Department of Radiology, Columbia University Vagelos College of Physicians and Surgeons, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, United States of America
| | - Mary M Salvatore
- Department of Radiology, Columbia University Vagelos College of Physicians and Surgeons, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, United States of America.
| |
Collapse
|
16
|
Lv X, Li Y, Wang B, Wang Y, Xu Z, Hou D. Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases. Eur J Radiol Open 2024; 12:100548. [PMID: 38298532 PMCID: PMC10827674 DOI: 10.1016/j.ejro.2024.100548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Abstract
Background Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. Methods This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. Results The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). Conclusions Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.
Collapse
Affiliation(s)
- Xinna Lv
- Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Ye Li
- Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Bing Wang
- Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Yichuan Wang
- Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Zexuan Xu
- Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Dailun Hou
- Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| |
Collapse
|
17
|
van der Reijd DJ, Guerendel C, Staal FCR, Busard MP, De Oliveira Taveira M, Klompenhouwer EG, Kuhlmann KFD, Moelker A, Verhoef C, Starmans MPA, Lambregts DMJ, Beets-Tan RGH, Benson S, Maas M. Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation. Eur Radiol 2024; 34:3635-3643. [PMID: 37987835 PMCID: PMC11166748 DOI: 10.1007/s00330-023-10417-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/07/2023] [Accepted: 09/10/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM). MATERIALS AND METHODS Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation. RESULTS The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively. CONCLUSION The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation. CLINICAL RELEVANCE STATEMENT Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts. KEY POINTS • Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.
Collapse
Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Corentin Guerendel
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Femke C R Staal
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Milou P Busard
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Mateus De Oliveira Taveira
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Elisabeth G Klompenhouwer
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Koert F D Kuhlmann
- Department of Surgery, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Adriaan Moelker
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Campusvej 55, DK 5230, Odense M, Denmark
| | - Sean Benson
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| |
Collapse
|
18
|
Li Z, Liu X, Gao Y, Lu X, Lei J. Ultrasound-based radiomics for early predicting response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review with meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:934-944. [PMID: 38630147 DOI: 10.1007/s11547-024-01783-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 01/10/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE This study aims to evaluate the diagnostic accuracy of ultrasound imaging (US)-based radiomics for the early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS We comprehensively searched PubMed, Cochrane Library, Embase, and Web of Science databases up to 1 January 2023 for eligible studies. We assessed the methodological quality of the enrolled studies with Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. We performed meta-analyses to summarize the diagnostic efficacy of US-based radiomics in response to NAC in breast cancer patients. RESULTS Eight studies proved eligible. Eligible studies exhibited an average RQS score of 12.88 (35.8% of the total score), with the RQS score ranging from 8 to 19. In the meta-analyses, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.87 (95% CI 0.81-0.92), 0.78 (95% CI 0.72-0.83), 4.02 (95% CI 3.18-5.08), 0.16 (95% CI 0.10-0.25), and 25.17 (95% CI 15.10-41.95), respectively. Results from subgroup analyses indicated that prospective studies apparently exhibited more optimal sensitivity than retrospective studies. Sensitivity analyses exhibited similar results to the primary analyses. CONCLUSION US-based radiomics may be a potentially crucial adjuvant method for evaluating the response of breast cancer to NAC. Due to limited data available and low quality of eligible studies, more multicenter prospective studies with rigorous methods are required to confirm our findings.
Collapse
Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Xinran Liu
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Xingru Lu
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, 730000, China.
| |
Collapse
|
19
|
Fang S, Wang Y, He Y, Yu T, Xie Y, Cai Y, Li W, Wang Y, Huang Z. Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions. Otolaryngol Head Neck Surg 2024; 170:1561-1569. [PMID: 38557958 DOI: 10.1002/ohn.744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE This study aims to use machine learning techniques together with radiomics methods to build a preoperative predictive diagnostic model from spiral computed tomography (CT) images. The model is intended for the differential diagnosis of common jaw cystic lesions. STUDY DESIGN Retrospective, case-control study. SETTING This retrospective study was conducted at Sun Yat-sen Memorial Hospital of Sun Yat-sen University (Guangzhou, Guangdong, China). All the data used to build the predictive diagnostic model were collected from 160 patients, who were treated at the Department of Oral and Maxillofacial Surgery at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 2019 and 2023. METHODS We included a total of 160 patients in this study. We extracted 107 radiomic features from each patient's CT scan images. After a feature selection process, we chose 15 of these radiomic features to construct the predictive diagnostic model. RESULTS Among the preoperative predictive diagnostic models built using 3 different machine learning methods (support vector machine, random forest [RF], and multivariate logistic regression), the RF model showed the best predictive performance. It demonstrated a sensitivity of 0.923, a specificity of 0.643, an accuracy of 0.825, and an area under the receiver operating characteristic curve of 0.810. CONCLUSION The preoperative predictive model, based on spiral CT radiomics and machine learning algorithms, shows promising differential diagnostic capabilities. For common jaw cystic lesions, this predictive model has potential clinical application value, providing a scientific reference for treatment decisions.
Collapse
Affiliation(s)
- Songling Fang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yuepeng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yilin He
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Taihui Yu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yutong Xie
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Yongkang Cai
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Wenhao Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Yan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| | - Zhiquan Huang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangdong, Guangzhou, China
| |
Collapse
|
20
|
Yang T, Liu D, Zhang Z, Sa R, Guan F. Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning Models. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:952-964. [PMID: 38321311 PMCID: PMC11169166 DOI: 10.1007/s10278-024-01007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 02/08/2024]
Abstract
This study aimed to examine the feasibility of utilizing radiomics models derived from 18F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone 18F-FDG PET/CT scans. Lesions were extracted from PET/CT and randomly divided into training and validation sets. Two different types of models were constructed as follows: features that are extracted from standardized uptake values (SUV)-associated parameters, and CT images were used to build SUV/CT-based model. Features that are derived from PET and CT images were used to build PET/CT-based model. Logistic regression (LR), linear support vector machine, support vector machine with the radial basis function kernel, neural networks, and adaptive boosting were performed as classifiers in each model. In the training sets, 77 patients, and 247 lesions were selected for building the models. In the validation sets, PET/CT-based model demonstrated better performance than that of SUV/CT-based model in the prediction of T-cell lymphoma. LR showed highest accuracy with 0.779 [0.697, 0.860], area under the receiver operating characteristic curve (AUC) with 0.863 [0.762, 0.963], and preferable goodness-of-fit in PET/CT-based model at the patient level. LR also showed best performance with accuracy of 0.838 [0.741, 0.936], AUC of 0.907 [0.839, 0.976], and preferable goodness-of-fit in PET/CT-based model at the lesion level. 18F-FDG PET/CT-based radiomics models with different machine learning classifiers were able to screen T-cell lymphoma in children with high accuracy, AUC, and preferable goodness-of-fit, providing incremental value compared with SUV-associated features.
Collapse
Affiliation(s)
- Taiyu Yang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Danyan Liu
- Department of Radiology, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Zexu Zhang
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China
| | - Ri Sa
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
| | - Feng Guan
- Department of Nuclear Medicine, The First Hospital of Jilin University, 1# Xinmin St, Changchun, 130021, China.
| |
Collapse
|
21
|
Netprasert SA, Khongwirotphan S, Seangsawang R, Patipipittana S, Jantarabenjakul W, Puthanakit T, Chintanapakdee W, Sriswasdi S, Rakvongthai Y. Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics. Radiol Phys Technol 2024; 17:467-475. [PMID: 38668939 DOI: 10.1007/s12194-024-00803-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 05/27/2024]
Abstract
The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.
Collapse
Affiliation(s)
- Sa-Angtip Netprasert
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn, University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sararas Khongwirotphan
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Roongprai Seangsawang
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Supanuch Patipipittana
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Watsamon Jantarabenjakul
- Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Pediatric Infectious Diseases and Vaccines, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Thanyawee Puthanakit
- Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Pediatric Infectious Diseases and Vaccines, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wariya Chintanapakdee
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand.
- Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| |
Collapse
|
22
|
Xie Q, Zhao Z, Yang Y, Wang X, Wu W, Jiang H, Hao W, Peng R, Luo C. A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection. Cancer Med 2024; 13:e7374. [PMID: 38864473 PMCID: PMC11167608 DOI: 10.1002/cam4.7374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 04/21/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
PURPOSE Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. MATERIALS AND METHODS We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. RESULTS Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. CONCLUSIONS The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.
Collapse
Affiliation(s)
- Qu Xie
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
- Wenzhou Medical UniversityWenzhouZhejiangChina
| | - Zeyin Zhao
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan UniversityChangshaHunanChina
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Yanzhen Yang
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
- Wenzhou Medical UniversityWenzhouZhejiangChina
| | - Xiaohong Wang
- Department of Intestinal OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Wei Wu
- Department of PathologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Haitao Jiang
- Department of RadiologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Weiyuan Hao
- Department of InterventionZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Ruizi Peng
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Cong Luo
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| |
Collapse
|
23
|
Li B, Su J, Liu K, Hu C. Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer. Eur J Radiol Open 2024; 12:100549. [PMID: 38304572 PMCID: PMC10831499 DOI: 10.1016/j.ejro.2024.100549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 01/03/2024] [Accepted: 01/14/2024] [Indexed: 02/03/2024] Open
Abstract
Purpose Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC. Methods A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890-0.986) compared with the radiomics model (0.829, 95%CI: 0.738-0.898) and the deep learning model (0.935, 95%CI: 0.865-0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779-0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628-0.897) and the deep learning model (0.867, 95% CI: 0.724-0.952). Conclusion The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.
Collapse
Affiliation(s)
| | | | - Kai Liu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
| | - Chunfeng Hu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|
24
|
Li C, Chen X, Chen C, Gong Z, Pataer P, Liu X, Lv X. Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101840. [PMID: 38548062 DOI: 10.1016/j.jormas.2024.101840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/07/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement. RESULTS A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03). CONCLUSIONS The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.
Collapse
Affiliation(s)
- Chenxi Li
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China; Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, School of Stomatology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan 430022, PR China.
| | - Xinya Chen
- College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China
| | - Cheng Chen
- College of Software, Xinjiang University. Urumqi 830046, PR China
| | - Zhongcheng Gong
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China.
| | - Parekejiang Pataer
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China
| | - Xu Liu
- Department of Maxillofacial Surgery, Hospital of Stomatology, Key Laboratory of Dental-Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, Faculty of Dentistry, Lanzhou University. Lanzhou 730013, PR China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China; College of Software, Xinjiang University. Urumqi 830046, PR China
| |
Collapse
|
25
|
Huang D, Yang X, Ruan H, Zhuo Y, Yuan K, Ruan B, Li F. Enhancing Prediction of Myocardial Recovery After Coronary Revascularization: Integrating Radiomics from Myocardial Contrast Echocardiography with Machine Learning. Int J Gen Med 2024; 17:2539-2555. [PMID: 38841127 PMCID: PMC11151281 DOI: 10.2147/ijgm.s465023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024] Open
Abstract
Introduction Chronic coronary artery disease (CAD) management often relies on myocardial contrast echocardiography (MCE), yet its effectiveness is limited by subjective interpretations and difficulty in distinguishing hibernating from necrotic myocardium. This study explores the integration of machine learning (ML) with radiomics to predict functional recovery in dyskinetic myocardial segments in CAD patients undergoing revascularization, aiming to overcome these limitations. Methods This prospective study enrolled 55 chronic CAD patients, dividing into training (39 patients, 205 segments) and testing sets (16 patients, 68 segments). Dysfunctional myocardial segments were identified by initial wall motion scores (WMS) of ≥2 (hypokinesis or higher). Functional recovery was defined as a decrease of ≥1 grade in WMS during follow-up echocardiography. Radiomics features were extracted from dyssynergic segments in end-systolic phase MCE images across five cardiac cycles post- "flash" impulse and processed through a five-step feature selection. Four ML classifiers were trained and compared using these features and MCE parameters, to identify the optimal model for myocardial recovery prediction. Results Functional improvement was noted in 139 out of 273 dyskinetic segments (50.9%) following revascularization. Receiver Operating Characteristic (ROC) analysis determined that myocardial blood flow (MBF) was the most precise clinical predictor of recovery, with an area under the curve (AUC) of 0.770. Approximately 1.34 million radiomics features were extracted, with nine features identified as key predictors of myocardial recovery. The random forest (RF) model, integrating MBF values and radiomics features, demonstrated superior predictive accuracy over other ML classifiers. Validation of the RF model on the testing dataset demonstrated its effectiveness, evidenced by an AUC of 0.821, along with consistent calibration and clinical utility. Conclusion The integration of ML with radiomics from MCE effectively predicts myocardial recovery in CAD. The RF model, combining radiomics and MBF values, presents a non-invasive, precise approach, significantly enhancing CAD management.
Collapse
Affiliation(s)
- Deyi Huang
- Department of Ultrasound, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| | - Xingan Yang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Linhai City, Zhejiang Province, People’s Republic of China
| | - Hongbiao Ruan
- Department of Cardiology, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| | - Yushui Zhuo
- Department of Ultrasound, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| | - Kai Yuan
- Department of Clinical Laboratory, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| | - Bowen Ruan
- Department of Ultrasound, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| | - Fang Li
- Department of Ultrasound, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China
| |
Collapse
|
26
|
Fu Y, Zhou J, Li J. Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis. PLoS One 2024; 19:e0303669. [PMID: 38820391 PMCID: PMC11142607 DOI: 10.1371/journal.pone.0303669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/29/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. OBJECTIVE This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. METHODS A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. RESULTS Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. CONCLUSION Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.
Collapse
Affiliation(s)
- Yuxia Fu
- Department of Ultrasound, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Jialin Zhou
- Department of Ultrasound, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Junfeng Li
- Department of Oncology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| |
Collapse
|
27
|
Xue H, Lu H, Wang Y, Li N, Wang G. MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging. PLoS One 2024; 19:e0304419. [PMID: 38820482 PMCID: PMC11142489 DOI: 10.1371/journal.pone.0304419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 05/12/2024] [Indexed: 06/02/2024] Open
Abstract
In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.
Collapse
Affiliation(s)
- Han Xue
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Huimin Lu
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Yilong Wang
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
- The First Hospital of Jilin University, Changchun, Jilin, China
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China
| | - Niya Li
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Guizeng Wang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| |
Collapse
|
28
|
Zhang AB, Zhao JR, Wang S, Xue J, Zhang JY, Sun ZP, Sun LS, Li TJ. CT-based radiomics analysis of different machine learning models for differentiating gnathic fibrous dysplasia and ossifying fibroma. Oral Dis 2024. [PMID: 38813877 DOI: 10.1111/odi.14984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/15/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024]
Abstract
OBJECTIVE In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery. MATERIALS AND METHODS We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models' robustness and clinical value. RESULTS We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy. CONCLUSION Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.
Collapse
Affiliation(s)
- Ao-Bo Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
| | - Jun-Ru Zhao
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
| | - Shuo Wang
- Department of stomatology, Shandong Public Health Clinical Center, Jinan, Shandong, China
| | - Jiang Xue
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
| | - Jian-Yun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
| | - Zhi-Peng Sun
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
| | - Li-Sha Sun
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China
| | - Tie-Jun Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China
| |
Collapse
|
29
|
Wang Y, Zhao H, Fu P, Tian L, Su Y, Lyu Z, Gu W, Wang Y, Liu S, Wang X, Zheng H, Du J, Zhang R. Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis. Med Phys 2024. [PMID: 38801340 DOI: 10.1002/mp.17193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.
Collapse
Affiliation(s)
- Yan Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yang Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Shan Liu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xi Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Han Zheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jingjing Du
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Zhang
- Department of Magnetic Resonance, The First Hospital of Qiqihar, Qiqihar, Heilongjiang, China
| |
Collapse
|
30
|
Deng L, Shuai P, Liu Y, Yong T, Liu Y, Li H, Zheng X. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis. Osteoporos Int 2024:10.1007/s00198-024-07136-y. [PMID: 38802557 DOI: 10.1007/s00198-024-07136-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
This study aimed to assess the diagnostic accuracy of radiomics for predicting osteoporosis and the quality of radiomic studies. The study protocol was prospectively registered on PROSPERO (CRD42023425058). We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from inception to June 1, 2023, for eligible articles that applied radiomic techniques to diagnosing osteoporosis or abnormal bone mass. Quality and risk of bias of the included studies were evaluated with radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tools. The data analysis utilized the R program with mada, metafor, and meta packages. Ten retrospective studies with 5926 participants were included in the systematic review and meta-analysis. The overall risk of bias and applicability concerns for each domain of the studies were rated as low, except for one study which was considered to have a high risk of flow and time bias. The mean METRICS score was 70.1% (range 49.6-83.2%). There was moderate heterogeneity across studies and meta-regression identified sources of heterogeneity in the data, including imaging modality, feature selection method, and classifier. The pooled diagnostic odds ratio (DOR) under the bivariate random effects model across the studies was 57.22 (95% CI 27.62-118.52). The pooled sensitivity and specificity were 87% (95% CI 81-92%) and 87% (95% CI 77-93%), respectively. The area under the summary receiver operating characteristic curve (AUC) of the radiomic models was 0.94 (range 0.8 to 0.98). The results supported that the radiomic techniques had good accuracy in diagnosing osteoporosis or abnormal bone mass. The application of radiomics in osteoporosis diagnosis needs to be further confirmed by more prospective studies with rigorous adherence to existing guidelines and multicenter validation.
Collapse
Affiliation(s)
- Ling Deng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Shuai
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Youren Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Yong
- Department of Medical Information Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuping Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaoxia Zheng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| |
Collapse
|
31
|
Xing X, Li D, Peng J, Shu Z, Zhang Y, Song Q. A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer. Sci Rep 2024; 14:11760. [PMID: 38783014 PMCID: PMC11116457 DOI: 10.1038/s41598-024-62584-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
This study aimed to develop an optimal radiomics model for preoperatively predicting microsatellite instability (MSI) in patients with rectal cancer (RC) based on multiparametric magnetic resonance imaging. The retrospective study included 308 RC patients who did not receive preoperative antitumor therapy, among whom 51 had MSI. Radiomics features were extracted and dimensionally reduced from T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), and T1-weighted contrast enhanced (T1CE) images for each patient, and the features of each sequence were combined. Multifactor logistic regression was used to screen the optimal feature set for each combination. Different machine learning methods were applied to construct predictive MSI status models. Relative standard deviation values were determined to evaluate model performance and select the optimal model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate model performance. The model constructed using the k-nearest neighbor (KNN) method combined with T2WI and T1CE images performed best. The area under the curve values for prediction of MSI with this model were 0.849 (0.804-0.887), with a sensitivity of 0.784 and specificity of 0.805. The Delong test showed no significant difference in diagnostic efficacy between the KNN-derived model and the traditional logistic regression model constructed using T1WI + DWI + T1CE and T2WI + T1WI + DWI + T1CE data (P > 0.05) and the diagnostic efficiency of the KNN-derived model was slightly better than that of the traditional model. From ROC curve analysis, the KNN-derived model significantly distinguished patients at low- and high-risk of MSI with the optimal threshold of 0.2, supporting the clinical applicability of the model. The model constructed using the KNN method can be applied to noninvasively predict MSI status in RC patients before surgery based on radiomics features from T2WI and T1CE images. Thus, this method may provide a convenient and practical tool for formulating treatment strategies and optimizing individual clinical decision-making for patients with RC.
Collapse
Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dongxue Li
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiaowei Song
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
| |
Collapse
|
32
|
Qian H, Huang Y, Xu L, Fu H, Lu B. Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics. Sci Rep 2024; 14:11538. [PMID: 38773179 PMCID: PMC11109225 DOI: 10.1038/s41598-024-62457-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 05/16/2024] [Indexed: 05/23/2024] Open
Abstract
Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the Ki67 subgroup, and 0.849 (0.667-1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.
Collapse
Affiliation(s)
- Hongwei Qian
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, People's Republic of China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People's Republic of China
| | - Yanhua Huang
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, People's Republic of China
| | - Luohang Xu
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, People's Republic of China
| | - Hong Fu
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, People's Republic of China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People's Republic of China
| | - Baochun Lu
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, People's Republic of China.
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People's Republic of China.
| |
Collapse
|
33
|
O'Sullivan NJ, Temperley HC, Horan MT, Kamran W, Corr A, O'Gorman C, Saadeh F, Meaney JM, Kelly ME. Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04330-8. [PMID: 38744703 DOI: 10.1007/s00261-024-04330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
Collapse
Affiliation(s)
- Niall J O'Sullivan
- Department of Radiology, St. James's Hospital, Dublin, Ireland.
- School of Medicine, Trinity College Dublin, Dublin, Ireland.
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland.
| | | | - Michelle T Horan
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Waseem Kamran
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James's Hospital, Dublin, Ireland
| | | | - Feras Saadeh
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - James M Meaney
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Michael E Kelly
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- Department of Surgery, St. James's Hospital, Dublin, Ireland
| |
Collapse
|
34
|
Wang H, Sang L, Xu J, Huang C, Huang Z. Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer. Heliyon 2024; 10:e29875. [PMID: 38720718 PMCID: PMC11076642 DOI: 10.1016/j.heliyon.2024.e29875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Objective To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC). Methods Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model's predictive efficacy was made using AUC. Results On the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884). Conclusion The nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.
Collapse
Affiliation(s)
- Haili Wang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Li Sang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| |
Collapse
|
35
|
Obuchowicz R, Strzelecki M, Piórkowski A. Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing-A Review. Cancers (Basel) 2024; 16:1870. [PMID: 38791949 PMCID: PMC11120567 DOI: 10.3390/cancers16101870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 04/28/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) is currently becoming a leading field in data processing [...].
Collapse
Affiliation(s)
- Rafał Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-008 Krakow, Poland;
| | - Michał Strzelecki
- Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland
| | - Adam Piórkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland
| |
Collapse
|
36
|
Zhang X, Chen S, Zhang P, Wang C, Wang Q, Zhou X. Staging of Liver Fibrosis Based on Energy Valley Optimization Multiple Stacking (EVO-MS) Model. Bioengineering (Basel) 2024; 11:485. [PMID: 38790352 PMCID: PMC11117710 DOI: 10.3390/bioengineering11050485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Currently, staging the degree of liver fibrosis predominantly relies on liver biopsy, a method fraught with potential risks, such as bleeding and infection. With the rapid development of medical imaging devices, quantification of liver fibrosis through image processing technology has become feasible. Stacking technology is one of the effective ensemble techniques for potential usage, but precise tuning to find the optimal configuration manually is challenging. Therefore, this paper proposes a novel EVO-MS model-a multiple stacking ensemble learning model optimized by the energy valley optimization (EVO) algorithm to select most informatic features for fibrosis quantification. Liver contours are profiled from 415 biopsied proven CT cases, from which 10 shape features are calculated and inputted into a Support Vector Machine (SVM) classifier to generate the accurate predictions, then the EVO algorithm is applied to find the optimal parameter combination to fuse six base models: K-Nearest Neighbors (KNNs), Decision Tree (DT), Naive Bayes (NB), Extreme Gradient Boosting (XGB), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF), to create a well-performing ensemble model. Experimental results indicate that selecting 3-5 feature parameters yields satisfactory results in classification, with features such as the contour roundness non-uniformity (Rmax), maximum peak height of contour (Rp), and maximum valley depth of contour (Rm) significantly influencing classification accuracy. The improved EVO algorithm, combined with a multiple stacking model, achieves an accuracy of 0.864, a precision of 0.813, a sensitivity of 0.912, a specificity of 0.824, and an F1-score of 0.860, which demonstrates the effectiveness of our EVO-MS model in staging the degree of liver fibrosis.
Collapse
Affiliation(s)
- Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (X.Z.); (P.Z.); (C.W.)
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China
| | - Shengxiang Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (X.Z.); (P.Z.); (C.W.)
| | - Pengfei Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (X.Z.); (P.Z.); (C.W.)
| | - Chun Wang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (X.Z.); (P.Z.); (C.W.)
| | - Qibo Wang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (X.Z.); (P.Z.); (C.W.)
| | - Xiangrong Zhou
- Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, Japan;
| |
Collapse
|
37
|
Wu L, Cen C, Yue X, Chen L, Wu H, Yang M, Lu Y, Ma L, Li X, Wu H, Zheng C, Han P. A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma. Cancer Imaging 2024; 24:55. [PMID: 38725034 PMCID: PMC11080083 DOI: 10.1186/s40644-024-00700-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.
Collapse
Affiliation(s)
- Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Xiaofei Yue
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Lei Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Hongying Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Ming Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Yuting Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Ling Ma
- Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, 610041, The People's Republic of China
| | - Xin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Heshui Wu
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China.
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China.
| |
Collapse
|
38
|
Zhao K, Seeliger E, Niendorf T, Liu Z. Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope? J Magn Reson Imaging 2024; 59:1494-1513. [PMID: 37675919 DOI: 10.1002/jmri.29000] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Kaixuan Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Erdmann Seeliger
- Institute of Translational Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|
39
|
Yang TH, Su YY, Tsai CL, Lin KH, Lin WY, Sung SF. Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke. Eur J Radiol 2024; 174:111405. [PMID: 38447430 DOI: 10.1016/j.ejrad.2024.111405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/05/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores. METHOD This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance. RESULTS The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001). CONCLUSIONS Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.
Collapse
Affiliation(s)
- Tzu-Hsien Yang
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Ying-Ying Su
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Chia-Ling Tsai
- Computer Science Department, Queens College, City University of New York, Flushing, NY, USA
| | - Kai-Hsuan Lin
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wei-Yang Lin
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi, Taiwan.
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; Department of Beauty & Health Care, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan.
| |
Collapse
|
40
|
Lu KC, Hung KC, Liao MT, Shih LJ, Chao CT. Vascular Calcification Heterogeneity from Bench to Bedside: Implications for Manifestations, Pathogenesis, and Treatment Considerations. Aging Dis 2024:AD.2024.0289. [PMID: 38739930 DOI: 10.14336/ad.2024.0289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/20/2024] [Indexed: 05/16/2024] Open
Abstract
Vascular calcification (VC) is the ectopic deposition of calcium-containing apatite within vascular walls, exhibiting a high prevalence in older adults, and those with diabetes or chronic kidney disease. VC is a subclinical cardiovascular risk trait that increases mortality and functional deterioration. However, effective treatments for VC remain largely unavailable despite multiple attempts. Part of this therapeutic nihilism results from the failure to appreciate the diversity of VC as a pathological complex, with unforeseeable variations in morphology, risk associates, and anatomical and molecular pathogenesis, affecting clinical management strategies. VC should not be considered a homogeneous pathology because accumulating evidence refutes its conceptual and content uniformity. Here, we summarize the pathophysiological sources of VC heterogeneity from the intersecting pathways and networks of cellular, subcellular, and molecular crosstalk. Part of these pathological connections are synergistic or mutually antagonistic. We then introduce clinical implications related to the VC heterogeneity concept. Even within the same individual, a specific artery may exhibit the strongest tendency for calcification compared with other arteries. The prognostic value of VC may only be detectable with a detailed characterization of calcification morphology and features. VC heterogeneity is also evident, as VC risk factors vary between different arterial segments and layers. Therefore, diagnostic and screening strategies for VC may be improved based on VC heterogeneity, including the use of radiomics. Finally, pursuing a homogeneous treatment strategy is discouraged and we suggest a more rational approach by diversifying the treatment spectrum. This may greatly benefit subsequent efforts to identify effective VC therapeutics.
Collapse
Affiliation(s)
- Kuo-Cheng Lu
- Division of Nephrology, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Fu Jen Catholic University Hospital, School of Medicine, Fu Jen Catholic University, New Taipei, Taiwan
| | - Kuo-Chin Hung
- Division of Nephrology, Department of Internal Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan
- Department of Pharmacy, Tajen University, Pingtung, Taiwan
| | - Min-Tser Liao
- Department of Pediatrics, Taoyuan Armed Forces General Hospital, Hsinchu Branch, Hsinchu, Taiwan
- Department of Pediatrics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Li-Jane Shih
- Department of Medical Laboratory, Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan
- Graduate Institute of Medical Science, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Ter Chao
- Division of Nephrology, Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Toxicology, National Taiwan University College of Medicine, Taipei, Taiwan
- Center of Faculty Development, National Taiwan University College of Medicine, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| |
Collapse
|
41
|
Haider SP, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann BH, Judson BL, Prasad ML, Burtness B, Aboian M, Canis M, Reichel CA, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers. J Nucl Med 2024; 65:803-809. [PMID: 38514087 DOI: 10.2967/jnumed.123.266637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.
Collapse
Affiliation(s)
- Stefan P Haider
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany;
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Tal Zeevi
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Kariem Sharaf
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Moritz Gross
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
- Charité Center for Diagnostic and Interventional Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Amit Mahajan
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Benjamin H Kann
- Department of Radiation Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Benjamin L Judson
- Division of Otolaryngology, Yale School of Medicine, New Haven, Connecticut
| | - Manju L Prasad
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut; and
| | - Barbara Burtness
- Section of Medical Oncology, Yale School of Medicine, New Haven, Connecticut
| | - Mariam Aboian
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Martin Canis
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Christoph A Reichel
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Philipp Baumeister
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | | |
Collapse
|
42
|
Fang D, Zhou Z, Xiong Y, Fan Y, Li Y, Zhao H, Huang J, Yuan G, Rao M. Advancing Alzheimer's research: Radiomics visualization of the default mode network in cerebral perfusion imaging. J Appl Clin Med Phys 2024; 25:e14368. [PMID: 38657114 PMCID: PMC11087173 DOI: 10.1002/acm2.14368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/05/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE Alzheimer's disease, an irreversible neurological condition, demands timely diagnosis for effective clinical intervention. This study employs radiomics analysis to assess image features in default mode network cerebral perfusion imaging among individuals with cognitive impairment. METHODS A radiomics analysis of cerebral perfusion imaging was conducted on 117 patients with cognitive impairment. They were divided into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were employed to select and model image features, followed by logistic regression analysis of LASSO and Random Forest results. Diagnostic performance was assessed by calculating the area under the curve (AUC). RESULTS In the training set, LASSO achieved AUC of 0.978, Random Forest had an AUC of 0.933. In the validation set, LASSO had AUC of 0.859, Random Forest had AUC of 0.986. By conducting Logistic Regression analysis in combination with LASSO and Random Forest, we identified a total of five radiomics features, with four related to morphology and one to textural features, originating from the medial prefrontal cortex and middle temporal gyrus. In the training set, Logistic Regression achieved AUC of 0.911, while in the validation set, it attained AUC of 0.925. CONCLUSION The medial prefrontal cortex and middle temporal gyrus are the two brain regions within the default mode network that hold the highest significance for Alzheimer's disease diagnosis. Radiomics analysis contributes to the clinical assessment of Alzheimer's disease by delving into image data to extract deeper layers of information.
Collapse
Affiliation(s)
- Danzhou Fang
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Zhiming Zhou
- Department of RadiologySecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yalan Xiong
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yongzeng Fan
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yixuan Li
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Huayi Zhao
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Jiahui Huang
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Gengbiao Yuan
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Maohua Rao
- Department of Nuclear MedicineSecond Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| |
Collapse
|
43
|
Zhu T, Zhang S, Jiang W, Chai D, Mao J, Wei Y, Xiong J. A Multiplanar Radiomics Model Based on Cranial Ultrasound to Predict the White Matter Injury in Premature Infants and an Analysis of its Correlation With Neurodevelopment. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:899-911. [PMID: 38269595 DOI: 10.1002/jum.16419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/14/2023] [Accepted: 01/07/2024] [Indexed: 01/26/2024]
Abstract
OBJECTIVES To develop and evaluate a multiplanar radiomics model based on cranial ultrasound (CUS) to predict white matter injury (WMI) in premature infants and explore its correlation with neurodevelopment. METHODS We retrospectively reviewed 267 premature infants. The radiomics features were extracted from five standard sections of CUS. The Spearman's correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was applied to select features and build radiomics signature, and a multiplanar radiomics model was constructed based on the radiomics signature of five planes. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Infants with WMI were re-examined by ultrasound at 2 and 4 weeks after birth, and the recovery degree of WMI was evaluated using multiplanar radiomics. The relationship between WMI and the recovery degree and neurodevelopment was analyzed. RESULTS The AUC of the multiplanar radiomics in the training and validation sets were 0.94 and 0.91, respectively. The neurodevelopmental function scores in infants with WMI were significantly lower than those in healthy preterm infants and full-term newborns (P < .001). There were statistically significant differences in the neurodevelopmental function scores of infants between the 2- and 4-week lesion disappearance and 4-week lesion persistence (P < .001). CONCLUSIONS The multiplanar radiomics model showed a good performance in predicting the WMI of premature infants. It can not only provide objective and accurate results but also dynamically monitor the degree of recovery of WMI to predict the prognosis of premature infants.
Collapse
Affiliation(s)
- Ting Zhu
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Shuang Zhang
- Educational Technology and Information, Shenzhen Polytechnic University, Shenzhen, China
| | - Wei Jiang
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Dan Chai
- Department of Obstetrics, Shenzhen Hospital of Southern Medical University, Shenzhen, China
| | - Jiaoyu Mao
- Department of Neonatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yuya Wei
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Jiayu Xiong
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| |
Collapse
|
44
|
Zhao J, Zhan Y, Zhou Y, Yang Z, Xiong X, Ye Y, Yao B, Xu S, Peng Y, Xiao X, Zeng X, Zuo M, Dai X, Gong L. CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study. Abdom Radiol (NY) 2024; 49:1569-1583. [PMID: 38587628 DOI: 10.1007/s00261-024-04279-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.
Collapse
Affiliation(s)
- Jinhong Zhao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yuan Zhan
- Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi, 330029, China
| | - Zhili Yang
- Department of Ultrasound, Jiangxi Maternal and Child Health Hospital, Nanchang, Jiangxi, 330038, China
| | - Xiaoling Xiong
- Cancer Center Office, Jiangxi Cancer Hospital, Nanchang, Jiangxi, 330029, China
| | - Yinquan Ye
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Bin Yao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Shiguo Xu
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Yun Peng
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xiaoyi Xiao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Minjing Zuo
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Xijian Dai
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China.
| |
Collapse
|
45
|
Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc 2024; 155:364-378. [PMID: 38520421 DOI: 10.1016/j.adaj.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.
Collapse
|
46
|
Zhao W, Hu Z, Kazerooni AF, Körzdörfer G, Nittka M, Davatzikos C, Viswanath SE, Wang X, Badve C, Ma D. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Invest Radiol 2024; 59:359-371. [PMID: 37812483 PMCID: PMC10997475 DOI: 10.1097/rli.0000000000001026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. MATERIALS AND METHODS A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. RESULTS First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. CONCLUSIONS The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.
Collapse
Affiliation(s)
- Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Zheyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| |
Collapse
|
47
|
Sperling S, Dahl VN, Fløe A. Lung abscess: an update on the current knowledge and call for future investigations. Curr Opin Pulm Med 2024; 30:229-234. [PMID: 38411181 DOI: 10.1097/mcp.0000000000001058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
PURPOSE OF REVIEW Lung abscess is a severe and complex respiratory infection. The purpose of this review is to discuss recent publications on lung abscess, covering topics such as epidemiology, clinical presentation, diagnosis, and treatment and prevention. RECENT FINDINGS Risk factors associated with lung abscess include structural lung disease, poor dental hygiene, and ventilator-associated pneumonia, while concerns are now raised regarding the potential of electronic cigarettes use as a risk factor. The complexity of lung abscess is reflected by the relative high number of case reports describing rare and complex cases. Early transthoracic drainage could improve in-hospital outcomes, while next-generation sequencing could become an important tool in diagnostics and future clinical studies. SUMMARY High-quality evidence and guidelines to support treatment of lung abscess are lacking. Despite advancements, we call for prospective studies to evaluate the use of invasive procedures and antibiotic treatment regimens.
Collapse
Affiliation(s)
- Søren Sperling
- Department of Respiratory Diseases and Allergy
- Department of Clinical Medicine
| | | | | |
Collapse
|
48
|
Park CJ, Choi SH, Kim D, Kim SB, Han K, Ahn SS, Lee WH, Choi EC, Keum KC, Kim J. MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma. Eur Radiol 2024; 34:3151-3159. [PMID: 37926740 DOI: 10.1007/s00330-023-10389-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVES Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.
Collapse
Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dain Kim
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Si Been Kim
- Undergraduate School of Biomedical Engineering, Korea University College of Health Science, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sung Soo Ahn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Won Hee Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eun Chang Choi
- Department of Otorhinolaryngology, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ki Chang Keum
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| |
Collapse
|
49
|
Chen S, Gao F, Guo T, Jiang L, Zhang N, Wang X, Zheng J. Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study. Int J Surg 2024; 110:2970-2977. [PMID: 38445478 PMCID: PMC11093464 DOI: 10.1097/js9.0000000000001222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. METHODS A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. CONCLUSION Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.
Collapse
Affiliation(s)
- Siteng Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
| | - Feng Gao
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Tuanjie Guo
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Liren Jiang
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Ning Zhang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Xiang Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Junhua Zheng
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
| |
Collapse
|
50
|
Pepponi M, Berti V, Fasciglione E, Montanini F, Canu L, Hubele F, Abenavoli E, Briganti V, Rapizzi E, Charpiot A, Taieb D, Pacak K, Goichot B, Imperiale A. [ 68Ga]DOTATOC PET-derived radiomics to predict genetic background of head and neck paragangliomas: a pilot investigation. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06735-5. [PMID: 38687372 DOI: 10.1007/s00259-024-06735-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/23/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE To investigate the [68Ga]DOTATOC PET radiomic profile of head and neck paragangliomas (HNPGLs) and identify radiomic characteristics useful as predictors of succinate dehydrogenase genes (SDHx) pathogenic variants. METHODS Sporadic and SDHx HNPGL patients, who underwent [68Ga]DOTATOC PET/CT, were retrospectively included. HNPGLs were analyzed using LIFEx software, and extracted features were harmonized to correct for batch effects and confronted testing for multiple comparison. Stepwise discriminant analysis was conducted to remove redundancy and identify best discriminating features. ROC analysis was used to define optimal cut-offs. Multivariate decision-tree analysis was performed using CHAID method. RESULTS 34 patients harboring 60 HNPGLs (51 SDHx in 25 patients) were included. Three sporadic and nine SDHx HNPGLs were metastatic. At stepwise discriminant analysis, both GLSZM-Zone Size Non-Uniformity (ZSNU, reflecting tumor heterogeneity) and IB-TLSRE (total lesion somatostatin receptor expression) were independent predictors of genetic status, with 96.4% of lesions and 91.6% of patients correctly classified after cross validation (p < 0.001). Among non-metastatic patients, GLSZM-ZSNU and IB-TLSRE were significantly higher in sporadic than SDHx HNPGLs (p < 0.001). No differences were revealed in metastatic patients. Decision-tree analysis highlights multifocality and IB-TLSRE as useful variables, correctly identifying 6/9 sporadic and 24/25 SDHx patients. Model failed to classify one SDHA and three sporadic patients (2 metastatic). CONCLUSION Radiomics features GLSZM-ZSNU and IB-TLSRE appear to reflect HNPGLs SDHx status and tumor behavior (metastatic vs. non-metastatic). If validated, especially IB-TLSRE might represent a simple and time-efficient radiomic index for SDHx variants early screening and prediction of tumor behavior in HNPGL cases.
Collapse
Affiliation(s)
- Miriam Pepponi
- Nuclear Medicine and Molecular Imaging, ICANS, University Hospitals of Strasbourg, University of Strasbourg, Strasbourg, France
- Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Valentina Berti
- Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
- Nuclear Medicine Unit, Careggi University Hospital, Florence, Italy
| | - Elsa Fasciglione
- Endocrinology, Diabetology, Nutrition, University Hospitals of Strasbourg, Strasbourg University, Strasbourg, France
| | - Flavio Montanini
- Nuclear Medicine Unit, Careggi University Hospital, Florence, Italy
| | - Letizia Canu
- Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
- Centro di Ricerca e Innovazione sulle Patologie Surrenaliche, Careggi University Hospital, Florence, Italy
| | - Fabrice Hubele
- Nuclear Medicine and Molecular Imaging, ICANS, University Hospitals of Strasbourg, University of Strasbourg, Strasbourg, France
| | | | | | - Elena Rapizzi
- Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
- Centro di Ricerca e Innovazione sulle Patologie Surrenaliche, Careggi University Hospital, Florence, Italy
| | - Anne Charpiot
- Otolaryngology and Maxillofacial Surgery, University Hospitals of Strasbourg, Strasbourg, France
| | - David Taieb
- La Timone University Hospital, CERIMED, Aix-Marseille University, Marseille, France
| | - Karel Pacak
- Eunice Kennedy Shriver NICHD, National Institutes of Health, Bethesda, MD, USA
| | - Bernard Goichot
- Endocrinology, Diabetology, Nutrition, University Hospitals of Strasbourg, Strasbourg University, Strasbourg, France
| | - Alessio Imperiale
- Nuclear Medicine and Molecular Imaging, ICANS, University Hospitals of Strasbourg, University of Strasbourg, Strasbourg, France.
- IPHC, UMR 7178, CNRS/Unistra, Strasbourg, France.
- Médecine Nucléaire et Imagerie Moléculaire, ICANS 17, rue Albert Calmette, Strasbourg, 67093, France.
| |
Collapse
|