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Leng Y, Wang X, Zheng T, Peng F, Xiong L, Wang Y, Gong L. Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer. Sci Rep 2024; 14:12456. [PMID: 38816463 PMCID: PMC11139946 DOI: 10.1038/s41598-024-63369-1] [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: 05/30/2023] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Xiwen Wang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Tian Zheng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Fei Peng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Liangxia Xiong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, Shanghai, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China.
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Zhao L, Zhou W, Fu Y, Ge Y, Feng L, Wang X, Li Z, Chen W. Diagnostic value of one-stop CT energy spectrum and perfusion for angiogenesis in colon and rectum cancer. BMC Med Imaging 2024; 24:116. [PMID: 38773384 PMCID: PMC11106941 DOI: 10.1186/s12880-024-01291-8] [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: 11/03/2023] [Accepted: 05/06/2024] [Indexed: 05/23/2024] Open
Abstract
OBJECTIVE Evaluation of the predictive value of one-stop energy spectrum and perfusion CT parameters for microvessel density (MVD) in colorectal cancer cancer foci. METHODS Clinical and CT data of 82 patients with colorectal cancer confirmed by preoperative colonoscopy or surgical pathology in our hospital from September 2019 to November 2022 were collected and analyzed retrospectively. Energy spectrum CT images were measured using the Protocols general module of the GSI Viewer software of the GE AW 4.7 post-processing workstation to measure the CT values of the arterial and venous phase lesions and the neighboring normal intestinal wall in a single energy range of 40 kev∼140 kev, and the slopes of the energy spectrum curves (λ) were calculated between 40 kev-90 kev; Iodine concentration (IC), Water concentration (WC), Effective-Z (Eff-Z) and Normalized iodine concentration (NIC) were measured by placing a region of interest (ROI) on the iodine concentration map and water concentration map at the lesion and adjacent to the normal intestinal wall.Perfusion CT images were scanned continuously and dynamically using GSI Perfusion software and analyzed by applying CT Perfusion 4.0 software.Blood volume (BV), blood flow (BF), surface permeability (PS), time to peak (TTP), and mean transit time (MTT) were measured respectively in the lesion and adjacent normal colorectal wall. Based on the pathological findings, the tumors were divided into a low MVD group (MVD < 35/field of view, n = 52 cases) and a high MVD group (MVD ≥ 35/field of view, n = 30 cases) using a median of 35/field of view as the MVD grouping criterion. The collected data were statistically analyzed, the subjects' operating characteristic curve (ROC) was plotted, and the area under curve (AUC), sensitivity, specificity, and Yoden index were calculated for the predicted efficacy of each parameter of the energy spectrum and perfusion CT and the combined parameters. RESULTS The CT values, IC, NIC, λ, Eff-Z of 40kev∼140kev single energy in the arterial and venous phase of colorectal cancer in the high MVD group were higher than those in the low MVD group, and the differences were all statistically significant (p < 0.05). The AUC of each single-energy CT value in the arterial phase from 40 kev to 120 kev for determining the high or low MVD of colorectal cancer was greater than 0.8, indicating that arterial stage has a good predictive value for high or low MVD in colorectal cancer; AUC for arterial IC, NIC and IC + NIC were all greater than 0.9, indicating that in arterial colorectal cancer, both single and combined parameters of spectral CT are highly effective in predicting the level of MVD. The AUC of 40 kev to 90 kev single-energy CT values in the intravenous phase was greater than 0.9, and its diagnostic efficacy was more representative; The AUC of IC and NIC in venous stage were greater than 0.8, which indicating that the IC and NIC energy spectrum parameters in venous stage colorectal cancer have a very good predictive value for the difference between high and low MVDs, with the greatest diagnostic efficacy in IC.The values of BV and BF in the high MVD group were higher than those in the low MVD group, and the differences were statistically significant (P < 0.05), and the AUC of BF, BV, and BV + BF were 0.991, 0.733, and 0.997, respectively, with the highest diagnostic efficacy for determining the level of MVD in colorectal cancer by BV + BF. CONCLUSION One-stop CT energy spectrum and perfusion imaging technology can accurately reflect the MVD in living tumor tissues, which in turn reflects the tumor angiogenesis, and to a certain extent helps to determine the malignancy, invasion and metastasis of living colorectal cancer tumor tissues based on CT energy spectrum and perfusion parameters.
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Affiliation(s)
- Ling Zhao
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Wei Zhou
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Yu Fu
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Yanlei Ge
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Li Feng
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Xingwen Wang
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Zemao Li
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China
| | - Weibin Chen
- North China University Of Science And Technology Affiliated Hospital, Tangshan, Hebei, 063000, China.
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Altham C, Zhang H, Pereira E. Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review. PLoS One 2024; 19:e0303644. [PMID: 38753740 PMCID: PMC11098383 DOI: 10.1371/journal.pone.0303644] [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: 03/13/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. METHODS To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. RESULTS Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. CONCLUSIONS Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.
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Affiliation(s)
- Callum Altham
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Huaizhong Zhang
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Ella Pereira
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
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Diao Z, Jiang H. A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features. Comput Biol Med 2024; 174:108461. [PMID: 38626509 DOI: 10.1016/j.compbiomed.2024.108461] [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: 08/04/2023] [Revised: 03/21/2024] [Accepted: 04/07/2024] [Indexed: 04/18/2024]
Abstract
BACKGROUND Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.
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Affiliation(s)
- Zhaoshuo Diao
- Software College, Northeastern University, Shenyang 110819, China
| | - Huiyan Jiang
- Software College, Northeastern University, Shenyang 110819, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, China.
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Li Y, Wang JP, Zhu X. Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators. BMC Med Imaging 2024; 24:90. [PMID: 38627672 PMCID: PMC11020316 DOI: 10.1186/s12880-024-01265-w] [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: 09/21/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Wilson's disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics. METHODS A retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model's discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model. RESULTS Acoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model's predictive ability is 0.927 (95% CI: 0.88-0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model's rationality indicates that the model's AUC is greater than the AUC of using ALB, ARFI, and PVD alone. CONCLUSION The nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.
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Affiliation(s)
- Yan Li
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China.
| | - Jing Ping Wang
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China
| | - Xiaoli Zhu
- Department of Intervention, The First Affiliated Hospital of Soochow University, 899, The Pinghai Road, 215006, Jiangsu, P.R. China
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Kim H, Yoo SK, Kim JS, Kim YT, Lee JW, Kim C, Hong CS, Lee H, Han MC, Kim DW, Kim SY, Kim TM, Kim WH, Kong J, Kim YB. Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT. Sci Rep 2024; 14:8504. [PMID: 38605094 PMCID: PMC11009270 DOI: 10.1038/s41598-024-59014-6] [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: 10/10/2023] [Accepted: 04/05/2024] [Indexed: 04/13/2024] Open
Abstract
This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.
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Affiliation(s)
- Hojin Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sang Kyun Yoo
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yong Tae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jai Wo Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Changhwan Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Min Cheol Han
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Dong Wook Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Se Young Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Tae Min Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Woo Hyoung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jayoung Kong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, Korea.
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Duan L, Liu Z, Wan F, Dai B. Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects. BMC Cancer 2024; 24:448. [PMID: 38605339 PMCID: PMC11007899 DOI: 10.1186/s12885-024-12071-6] [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: 11/11/2023] [Accepted: 02/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Whole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects. METHODS An extensive PubMed search was conducted until February 26, 2023, with the search term "prostate", "whole-mount", "large format histology", which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding. RESULTS WMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region. CONCLUSIONS WMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.
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Affiliation(s)
- Lewei Duan
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China
| | - Zheng Liu
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China
| | - Fangning Wan
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China.
| | - Bo Dai
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China.
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Zhang XF, Wu HY, Liang XW, Chen JL, Li J, Zhang S, Liu Z. Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma. BMC Womens Health 2024; 24:182. [PMID: 38504245 PMCID: PMC10949581 DOI: 10.1186/s12905-024-03001-6] [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: 09/09/2023] [Accepted: 02/27/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).
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Grants
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
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Affiliation(s)
- Xue-Fang Zhang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Hong-Yuan Wu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Xu-Wei Liang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jia-Luo Chen
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jianpeng Li
- Radiology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Shihao Zhang
- Pathology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Zhigang Liu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
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Gong C, Huang Y, Luo M, Cao S, Gong X, Ding S, Yuan X, Zheng W, Zhang Y. Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images. Radiat Oncol 2024; 19:37. [PMID: 38486193 PMCID: PMC10938692 DOI: 10.1186/s13014-024-02429-2] [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: 07/06/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the absence of electron density information limits its further clinical application. Therefore, the aim of this study is to develop and evaluate a novel unsupervised network (cycleSimulationGAN) for unpaired MR-to-CT synthesis. METHODS The proposed cycleSimulationGAN in this work integrates contour consistency loss function and channel-wise attention mechanism to synthesize high-quality CT-like images. Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature representation capability of deep network and extract more effective features. The mean absolute error (MAE) of Hounsfield Units (HU), peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and structural similarity index (SSIM) were calculated between synthetic CT (sCT) and ground truth (GT) CT images to quantify the overall sCT performance. RESULTS One hundred and sixty nasopharyngeal carcinoma (NPC) patients who underwent volumetric-modulated arc radiotherapy (VMAT) were enrolled in this study. The generated sCT of our method were more consistent with the GT compared with other methods in terms of visual inspection. The average MAE, RMSE, PSNR, and SSIM calculated over twenty patients were 61.88 ± 1.42, 116.85 ± 3.42, 36.23 ± 0.52 and 0.985 ± 0.002 for the proposed method. The four image quality assessment metrics were significantly improved by our approach compared to conventional cycleGAN, the proposed cycleSimulationGAN produces significantly better synthetic results except for SSIM in bone. CONCLUSIONS We developed a novel cycleSimulationGAN model that can effectively create sCT images, making them comparable to GT images, which could potentially benefit the MRI-based treatment planning.
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Affiliation(s)
- Changfei Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Yuling Huang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Mingming Luo
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Shunxiang Cao
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Xiaochang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
- Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Jiangxi, PR China
| | - Shenggou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Xingxing Yuan
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
| | - Wenheng Zheng
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China.
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China.
- Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Jiangxi, PR China.
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Yue JY, Ji K, Liu HP, Wu QW, Liang CH, Gao JB. Evaluating the consistency in different methods for measuring left atrium diameters. BMC Med Imaging 2024; 24:57. [PMID: 38443826 PMCID: PMC10916282 DOI: 10.1186/s12880-024-01231-6] [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: 11/01/2023] [Accepted: 02/20/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND The morphological information of the pulmonary vein (PV) and left atrium (LA) is of immense clinical importance for effective atrial fibrillation ablation. The aim of this study is to examine the consistency in different LA diameter measurement techniques. METHODS Retrospective imaging data from 87 patients diagnosed with PV computed tomography angiography were included. The patients consisted of 50 males and 37 females, with an average age of (60.74 ± 8.70) years. Two physicians independently measured the anteroposterior diameter, long diameter, and transverse diameter of the LA using six different methods. Additionally, we recorded the post-processing time of the images. Physician 1 conducted measurements twice with a one-month interval between the measurements to assess intra-rater reliability. Using the intraclass correlation coefficient (ICC), the consistency of each LA diameter measurement by the two physicians was evaluated. We compared the differences in the LA diameter and the time consumed for measurements using different methods. This was done by employing the rank sum test of a randomized block design (Friedman M test) and the q test for pairwise comparisons among multiple relevant samples. RESULTS (1) The consistency of the measured LA diameter by the two physicians was strong or very strong. (2) There were statistical differences in the anteroposterior diameter, long diameter, and transverse diameter of LA assessed using different methods (χ2 = 222.28, 32.74, 293.83, P < 0.001). (3) Different methods for measuring the diameters of LA required different amounts of time (χ2 = 333.10, P < 0.001). CONCLUSION The results of left atrium (LA) diameter measurements conducted by different physicians were found to be reliable. However, the LA diameters obtained through various techniques exhibited variations. It was observed that measuring LA long diameters using only the VR (volume rendering) picture was the most clinically applicable method.
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Affiliation(s)
- Jun-Yan Yue
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui Henan Province, 453200, Xinxiang, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Erqi District, 450000, Zhengzhou, Henan Province, China
- Heart Center, The First Affiliated Hospital of Xinxiang Medical University, 453200, Henan Pro vince, Weihui, China
| | - Kai Ji
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui Henan Province, 453200, Xinxiang, China
| | - Hai-Peng Liu
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui Henan Province, 453200, Xinxiang, China
| | - Qing-Wu Wu
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui Henan Province, 453200, Xinxiang, China
| | - Chang-Hua Liang
- Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Weihui Henan Province, 453200, Xinxiang, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Erqi District, 450000, Zhengzhou, Henan Province, China.
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Yap BP, Ng BK. Coarse-to-fine visual representation learning for medical images via class activation maps. Comput Biol Med 2024; 171:108203. [PMID: 38430741 DOI: 10.1016/j.compbiomed.2024.108203] [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: 07/27/2023] [Revised: 01/29/2024] [Accepted: 02/19/2024] [Indexed: 03/05/2024]
Abstract
The value of coarsely labeled datasets in learning transferable representations for medical images is investigated in this work. Compared to fine labels which require meticulous effort to annotate, coarse labels can be acquired at a significantly lower cost and can provide useful training signals for data-hungry deep neural networks. We consider coarse labels in the form of binary labels differentiating a normal (healthy) image from an abnormal (diseased) image and propose CAMContrast, a two-stage representation learning framework for medical images. Using class activation maps, CAMContrast makes use of the binary labels to generate heatmaps as positive views for contrastive representation learning. Specifically, the learning objective is optimized to maximize the agreement within fixed crops of image-heatmap pair to learn fine-grained representations that are generalizable to different downstream tasks. We empirically validate the transfer learning performance of CAMContrast on several public datasets, covering classification and segmentation tasks on fundus photographs and chest X-ray images. The experimental results showed that our method outperforms other self-supervised and supervised pretrain methods in terms of data efficiency and downstream performance.
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Affiliation(s)
- Boon Peng Yap
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore; Centre for OptoElectronics and Biophotonics, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Beng Koon Ng
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore; Centre for OptoElectronics and Biophotonics, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
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Wang H, Chen W, Jiang S, Li T, Chen F, Lei J, Li R, Xi L, Guo S. Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research. Sci Rep 2024; 14:5020. [PMID: 38424285 PMCID: PMC10904744 DOI: 10.1038/s41598-024-55838-4] [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: 09/23/2023] [Accepted: 02/28/2024] [Indexed: 03/02/2024] Open
Abstract
The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660-947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.
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Affiliation(s)
- Hui Wang
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu, China
| | - Wei Chen
- Department of Ultrasound, The Ningxia Hui Autonomous Region People's Hospital, Yinchuan, Ningxia, China
| | - Shanshan Jiang
- Department of Advanced Technical Support, Clinical and Technical Support, Philips Healthcare, Xi'an, Shanxi, China
| | - Ting Li
- Department of Ultrasound, The Ningxia Hui Autonomous Region People's Hospital, Yinchuan, Ningxia, China
| | - Fei Chen
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Ruixia Li
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Lili Xi
- Department of Pharmacologic Bases, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Shunlin Guo
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
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Yin M, Xu C, Zhu J, Xue Y, Zhou Y, He Y, Lin J, Liu L, Gao J, Liu X, Shen D, Fu C. Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images. BMC Med Imaging 2024; 24:50. [PMID: 38413923 PMCID: PMC10900643 DOI: 10.1186/s12880-024-01211-w] [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: 05/11/2023] [Accepted: 01/24/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. METHODS Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. RESULTS A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. CONCLUSIONS Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Chao Xu
- Department of Radiotherapy, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- The 23th ward, Yangzhou Third People's Hospital, 225000, Yangzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Yuhan Xue
- Medical School, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yijia Zhou
- Medical School, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yu He
- Medical School, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, 215006, Suzhou, Jiangsu, China
| | - Dan Shen
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China.
| | - Cuiping Fu
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China.
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Shi Y, Fan H, Li L, Hou Y, Qian F, Zhuang M, Miao B, Fei S. The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis. World J Surg Oncol 2024; 22:40. [PMID: 38297303 PMCID: PMC10832162 DOI: 10.1186/s12957-024-03321-9] [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: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. METHODS We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. RESULTS Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). CONCLUSION ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
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Affiliation(s)
- Yiheng Shi
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Haohan Fan
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Li Li
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Yaqi Hou
- College of Nursing, Yangzhou University, Yangzhou, 225009, China
| | - Feifei Qian
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Mengting Zhuang
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Bei Miao
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Institute of Digestive Diseases, Xuzhou Medical University, 84 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
| | - Sujuan Fei
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China.
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Cheng Q, Lin H, Zhao J, Lu X, Wang Q. Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears. J Orthop Surg Res 2024; 19:99. [PMID: 38297322 PMCID: PMC10829177 DOI: 10.1186/s13018-024-04602-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/28/2024] [Indexed: 02/02/2024] Open
Abstract
OBJECTIVE To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model. METHODS In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis. RESULTS Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively. CONCLUSION The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.
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Affiliation(s)
- Qi Cheng
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Haoran Lin
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Jie Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Xiao Lu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Qiang Wang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China.
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Liu J, Corti A, Calareso G, Spadarella G, Licitra L, Corino VDA, Mainardi L. Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes. Heliyon 2024; 10:e24377. [PMID: 38312621 PMCID: PMC10835257 DOI: 10.1016/j.heliyon.2024.e24377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.
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Affiliation(s)
- Jiaying Liu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Gaia Spadarella
- Postgraduation School in Radiodiagnostics, University of Milan, Italy
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Lisa Licitra
- Head and Neck Cancer Medical Oncology Department, Fondazione IRCCS Instituto Nazionale dei Tumori di Milano, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Liu JJ, Shen WB, Qin QR, Li JW, Li X, Liu MY, Hu WL, Wu YY, Huang F. Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data. J Cancer Res Clin Oncol 2024; 150:33. [PMID: 38270703 PMCID: PMC10811045 DOI: 10.1007/s00432-024-05610-y] [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: 09/11/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics. METHODS The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results. RESULTS Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification. CONCLUSION We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.
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Affiliation(s)
- Jian-Jun Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Wen-Bin Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Qi-Rong Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
- Ma'anshan Center for Disease Control and Prevention, Ma'anshan, Anhui, China
| | - Jian-Wei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Xue Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Meng-Yu Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Wen-Lei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yue-Yang Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Fen Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
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Yu L, Zhang Z, Yi H, Wang J, Li J, Wang X, Bai H, Ge H, Zheng X, Ni J, Qi H, Guan Y, Xu W, Zhu Z, Xing L, Dekker A, Wee L, Traverso A, Ye Z, Yuan Z. A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study. Radiat Oncol 2024; 19:10. [PMID: 38254106 PMCID: PMC10802016 DOI: 10.1186/s13014-024-02402-z] [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: 06/17/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
OBJECTIVES Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT. METHODS Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups. RESULTS In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001). CONCLUSIONS The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.
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Affiliation(s)
- Lu Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Zhen Zhang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - HeQing Yi
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Jin Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Junyi Li
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Xiaofeng Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Hui Bai
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Hong Ge
- The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoli Zheng
- The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haoran Qi
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Yong Guan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Wengui Xu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
| | - Zhiyong Yuan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
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Bai A, Si M, Xue P, Qu Y, Jiang Y. Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2024; 24:13. [PMID: 38191361 PMCID: PMC10775443 DOI: 10.1186/s12911-023-02397-9] [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: 02/01/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time. METHODS Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386. RESULTS Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or > 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia). CONCLUSIONS Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.
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Affiliation(s)
- Anying Bai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yimin Qu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Li Z, Song L, Qin B, Li K, Shi Y, Wang H, Wang H, Ma N, Li J, Wang J, Li C. A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study. J Orthop Surg Res 2024; 19:38. [PMID: 38183110 PMCID: PMC10770936 DOI: 10.1186/s13018-023-04473-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/14/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Surgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI. METHODS A total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram. RESULTS The SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029-5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006-1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022-1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672-0.882) and 0.732 (95% CI 0.603-0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively. CONCLUSIONS Operative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.
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Affiliation(s)
- Zhi Li
- Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Lihua Song
- Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Baoju Qin
- Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Kun Li
- Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Yingtao Shi
- Operating Room, Xingtai General Hospital of North China Medical and Health Group, Xingtai, Hebei, China
| | - Hongqing Wang
- Department of Orthopedics, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Huiwang Wang
- Department of Orthopedics, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Nan Ma
- Department of Orthopedics, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China
| | - Jinlong Li
- Hebei Provincial Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China
| | - Jitao Wang
- Hebei Provincial Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People's Hospital of Hebei Medical University, Xingtai, Hebei, China.
| | - Chaozheng Li
- Department of Infection Management, North China Healthcare Group Xingtai General Hospital, Xingtai, Hebei, China.
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21
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Zeng Y, Wang X, Wu J, Wang L, Shi F, Shu J. Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features. BMC Med Imaging 2024; 24:7. [PMID: 38166729 PMCID: PMC10763420 DOI: 10.1186/s12880-023-01188-y] [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: 06/10/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. METHODS A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. RESULTS Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions. CONCLUSIONS Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.
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Affiliation(s)
- Yanyan Zeng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, 646000, Luzhou, China
| | - Xiaoyong Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, 646000, Luzhou, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, 200030, Shanghai, China
| | - Limin Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, 646000, Luzhou, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, 200030, Shanghai, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, 646000, Luzhou, China.
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Zhao T, Fu C, Song W, Sham CW. RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images. Bioengineering (Basel) 2023; 11:16. [PMID: 38247893 PMCID: PMC10813712 DOI: 10.3390/bioengineering11010016] [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: 11/10/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking layers, leading to repetitive computational tasks and unnecessarily large neural networks. Moreover, the lack of available ground truth data for SRCs hampers the advancement of segmentation techniques for these cells. In response, this paper introduces an efficient and accurate deep learning framework (RGGC-UNet), which is a UNet framework including our proposed residual ghost block with ghost coordinate attention, featuring an encoder-decoder structure tailored for the semantic segmentation of SRCs. We designed a novel encoder using the residual ghost block with proposed ghost coordinate attention. Benefiting from the utilization of ghost block and ghost coordinate attention in the encoder, the computational overhead of our model is effectively minimized. For practical application in pathological diagnosis, we have enriched the DigestPath 2019 dataset with fully annotated mask labels of SRCs. Experimental outcomes underscore that our proposed model significantly surpasses other leading-edge models in segmentation accuracy while ensuring computational efficiency.
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Affiliation(s)
- Tengfei Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Chong Fu
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
- Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang 110819, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
| | - Wei Song
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Chiu-Wing Sham
- School of Computer Science, The University of Auckland, Auckland 1142, New Zealand
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Blaj LA, Cucu AI, Tamba BI, Turliuc MD. The Role of the NF-kB Pathway in Intracranial Aneurysms. Brain Sci 2023; 13:1660. [PMID: 38137108 PMCID: PMC10871091 DOI: 10.3390/brainsci13121660] [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: 10/30/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
The pathophysiology of intracranial aneurysms (IA) has been proven to be closely linked to hemodynamic stress and inflammatory pathways, most notably the NF-kB pathway. Therefore, it is a potential target for therapeutic intervention. In the present review, we investigated alterations in the vascular smooth muscle cells (VSMCs), extracellular matrix, and endothelial cells by the mediators implicated in the NF-kB pathway that lead to the formation, growth, and rupture of IAs. We also present an overview of the NF-kB pathway, focusing on stimuli and transcriptional targets specific to IAs, as well as a summary of the current strategies for inhibiting NF-kB activation in IAs. Our report adds to previously reported data and future research directions for treating IAs using compounds that can suppress inflammation in the vascular wall.
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Affiliation(s)
- Laurentiu Andrei Blaj
- Department of Neurosurgery, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (L.A.B.); (M.D.T.)
- “Prof. Dr. N. Oblu” Emergency Clinical Hospital, 700309 Iasi, Romania
| | - Andrei Ionut Cucu
- “Prof. Dr. N. Oblu” Emergency Clinical Hospital, 700309 Iasi, Romania
- Faculty of Medicine and Biological Sciences, University Stefan cel Mare of Suceava, 720229 Suceava, Romania
| | - Bogdan Ionel Tamba
- Advanced Research and Development Center for Experimental Medicine (CEMEX), “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
- Department of Pharmacology, Clinical Pharmacology and Algesiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Mihaela Dana Turliuc
- Department of Neurosurgery, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (L.A.B.); (M.D.T.)
- “Prof. Dr. N. Oblu” Emergency Clinical Hospital, 700309 Iasi, Romania
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Allameh Z, Rouholamin S, Rasti S, Adibi A, Foroughi Z, Goharian M, Rad MR, Dabaghi GG. A transvaginal ultrasound-based diagnostic calculator for uterus post-cesarean scar defect. BMC Womens Health 2023; 23:558. [PMID: 37891612 PMCID: PMC10612219 DOI: 10.1186/s12905-023-02715-3] [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: 08/12/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND A cesarean scar defect (CSD) is incomplete healing of the myometrium at the site of a prior cesarean section (CS), complicating more than half of all cesarean sections. While transvaginal ultrasound (TVU) is the most common modality for diagnosing this defect, hysteroscopy remains the gold standard. We aimed to develop an efficient diagnostic tool for CSD among women with abnormal uterine bleeding (AUB) by integrating TVU findings and participants' demographic features. METHODS A single-center cross-sectional study was conducted on 100 premenopausal and non-pregnant women with a history of CS complaining of AUB without a known systemic or structural etiology. Each participant underwent a hysteroscopy followed by a TVU the next day. The defect dimensions in TVU, patients' age, and the number of previous CSs were integrated into a binary logistic regression model to evaluate their predictive ability for a hysteroscopy-confirmed CSD. RESULTS Hysteroscopy identified 74 (74%) participants with CSD. The variables age, the number of CSs, defect length, and defect width significantly contributed to the logistic regression model to diagnose CSD with odds ratios of 9.7, 0.7, 2.6, and 1.7, respectively. The developed model exhibited accuracy, sensitivity, and specificity of 88.00%, 91.89%, and 76.92%, respectively. The area under the receiver operating curve was 0.955 (P-value < 0.001). CONCLUSION Among non-pregnant women suspected of CSD due to AUB, looking at age, the number of previous CSs, and TVU-based defect width and length can efficiently rule CSD out.
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Affiliation(s)
- Zahra Allameh
- Department of Obstetrics and Gynecology, Isfahan University of Medical Sciences, Ostad Motahari St., Felezi Bridge, Isfahan, Iran
| | - Safoura Rouholamin
- Department of Obstetrics and Gynecology, Isfahan University of Medical Sciences, Ostad Motahari St., Felezi Bridge, Isfahan, Iran
| | - Sina Rasti
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Atoosa Adibi
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zahra Foroughi
- Department of Obstetrics and Gynecology, Isfahan University of Medical Sciences, Ostad Motahari St., Felezi Bridge, Isfahan, Iran
| | - Maryam Goharian
- Department of Obstetrics and Gynecology, Isfahan University of Medical Sciences, Ostad Motahari St., Felezi Bridge, Isfahan, Iran.
| | - Mehrdad Rabiee Rad
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Hussain S, Lafarga-Osuna Y, Ali M, Naseem U, Ahmed M, Tamez-Peña JG. Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics 2023; 24:401. [PMID: 37884877 PMCID: PMC10605943 DOI: 10.1186/s12859-023-05515-6] [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: 04/07/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico.
| | - Yareth Lafarga-Osuna
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, Australia
| | - Masroor Ahmed
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Jose Gerardo Tamez-Peña
- School of Medicine and Health Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
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Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107714. [PMID: 37473589 DOI: 10.1016/j.cmpb.2023.107714] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/19/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs. METHODS The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing. RESULTS Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm). CONCLUSIONS This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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Nomura Y, Hoshiyama M, Akita S, Naganishi H, Zenbutsu S, Matsuoka A, Ohnishi T, Haneishi H, Mitsukawa N. Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning. Sci Rep 2023; 13:16214. [PMID: 37758908 PMCID: PMC10533488 DOI: 10.1038/s41598-023-43503-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.
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Affiliation(s)
- Yukihiro Nomura
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan.
| | - Masato Hoshiyama
- Department of Medical Engineering, Faculty of Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Shinsuke Akita
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Hiroki Naganishi
- Department of Plastic Surgery, Saiseikai Yokohamashi Nanbu Hospital, 3-2-10 Konandai, Konan-ku, Yokohama City, Kanagawa, 234-0054, Japan
| | - Satoki Zenbutsu
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Ayumu Matsuoka
- Department of Gynecology and Maternal-Fetal Medicine, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Takashi Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY, 10065, USA
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Nobuyuki Mitsukawa
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
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Hajianfar G, Kalayinia S, Hosseinzadeh M, Samanian S, Maleki M, Sossi V, Rahmim A, Salmanpour MR. Prediction of Parkinson's disease pathogenic variants using hybrid Machine learning systems and radiomic features. Phys Med 2023; 113:102647. [PMID: 37579523 DOI: 10.1016/j.ejmp.2023.102647] [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: 08/11/2022] [Revised: 05/08/2023] [Accepted: 07/29/2023] [Indexed: 08/16/2023] Open
Abstract
PURPOSE In Parkinson's disease (PD), 5-10% of cases are of genetic origin with mutations identified in several genes such as leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA). We aim to predict these two gene mutations using hybrid machine learning systems (HMLS), via imaging and non-imaging data, with the long-term goal to predict conversion to active disease. METHODS We studied 264 and 129 patients with known LRRK2 and GBA mutations status from PPMI database. Each dataset includes 513 features such as clinical features (CFs), conventional imaging features (CIFs) and radiomic features (RFs) extracted from DAT-SPECT images. Features, normalized by Z-score, were univariately analyzed for statistical significance by the t-test and chi-square test, adjusted by Benjamini-Hochberg correction. Multiple HMLSs, including 11 features extraction (FEA) or 10 features selection algorithms (FSA) linked with 21 classifiers were utilized. We also employed Ensemble Voting (EV) to classify the genes. RESULTS For prediction of LRRK2 mutation status, a number of HMLSs resulted in accuracies of 0.98 ± 0.02 and 1.00 in 5-fold cross-validation (80% out of total data points) and external testing (remaining 20%), respectively. For predicting GBA mutation status, multiple HMLSs resulted in high accuracies of 0.90 ± 0.08 and 0.96 in 5-fold cross-validation and external testing, respectively. We additionally showed that SPECT-based RFs added value to the specific prediction of of GBA mutation status. CONCLUSION We demonstrated that combining medical information with SPECT-based imaging features, and optimal utilization of HMLS can produce excellent prediction of the mutations status in PD patients.
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Affiliation(s)
- Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada
| | - Samira Kalayinia
- Cardiogenetic Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sara Samanian
- Firoozgar Hospital Medical Genetics Laboratory, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Maleki
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Mohammad R Salmanpour
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
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Yang B, Li W, Wu X, Zhong W, Wang J, Zhou Y, Huang T, Zhou L, Zhou Z. Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics. Diagnostics (Basel) 2023; 13:2627. [PMID: 37627886 PMCID: PMC10453422 DOI: 10.3390/diagnostics13162627] [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: 07/07/2023] [Revised: 08/03/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures.
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Affiliation(s)
- Beisheng Yang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Wenjie Li
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Xiaojia Wu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Weijia Zhong
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Jing Wang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Yu Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Tianxing Huang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
| | - Lu Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
- Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
| | - Zhiming Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China; (B.Y.); (W.L.); (X.W.); (W.Z.); (J.W.); (Y.Z.); (T.H.); (L.Z.)
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