1
|
Alqahtani A, Bhattacharjee S, Almopti A, Li C, Nabi G. Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma-a step towards virtual biopsy. Int J Surg 2024; 110:01279778-990000000-01418. [PMID: 38704622 PMCID: PMC11175789 DOI: 10.1097/js9.0000000000001483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/09/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVES Upper tract urothelial carcinoma is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards. METHODS Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens. RESULTS Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The MultiLayer Panel showed 84% sensitivity and 93% specificity while predicting upper tract urothelial carcinoma grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms (e.g., Support Vector classifier) provided a highly accurate prediction while grading upper tract urothelial carcinoma compared to clinical features alone or ureteroscopic biopsy histopathology. CONCLUSION Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for upper tract urothelial carcinoma. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics. CLINICAL RELEVANCE Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy.
Collapse
Affiliation(s)
- Abdulsalam Alqahtani
- School of Medicine, Centre for Medical Engineering and Technology
- Radiology Department, College of Applied Medical Sciences, Najran University, Najran 55461, Kingdom of Saudi Arabia
| | - Sourav Bhattacharjee
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | | | - Chunhui Li
- School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK
| | - Ghulam Nabi
- School of Medicine, Centre for Medical Engineering and Technology
| |
Collapse
|
2
|
Xin S, Chen J, Dongming L, Wei X, Yiran H. Application of three-dimensional reconstruction of renal tumor vessels to guide laparoscopic partial nephrectomy of hilar tumors and non-hilar tumors under zero ischemia. Asian J Surg 2024; 47:216-221. [PMID: 37574367 DOI: 10.1016/j.asjsur.2023.07.077] [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: 04/25/2022] [Revised: 04/09/2023] [Accepted: 07/16/2023] [Indexed: 08/15/2023] Open
Abstract
OBJECTIVE To investigate the safety and efficacy of three-dimensional reconstruction of renal tumor vessels to guide laparoscopic partial nephrectomy of hilar tumors and non-hilar tumors under zero ischemia. METHODS The clinical data of 82 patients with renal cancer who underwent zero ischemia retroperitoneal laparoscopic partial nephrectomy in the department of urology of our hospital from January 2018 to January 2021 were retrospectively reviewed. The patients were divided into hilar group and non-hilar group. The clinical data of all patients were statistically analyzed by t-test or χ2. RESULTS There was no significantly difference in gender, age, tumor diameter and pathological stage between hilar and non-hilar tumor group. Most of the target vessels in the hilar tumor group were single targets, while most of the target vessels in the non-hilar tumor group were multiple targets (P<0.05). There was no significantly difference between the groups for mean operative time and length of stay. But hilar tumor group had significantly longer operation time (109.3 ± 9.2 vs. 90.3 ± 9.5 min, p<0.001). There was no significant difference in renal GFR and serum creatinine between the two groups. Hilar tumor group had no significantly difference of change of creatinine and GFR at post-operative 6 and 12 months as compared with non-hilar tumor group. There were no bleeding, urinary leakage, infection and other related complications in the two groups after 1 month follow-up. After 12 months of follow-up, there was no tumor recurrence and metastasis in the two groups. CONCLUSION The application of three-dimensional renal tumor vascular reconstruction technology can better realize laparoscopic zero ischemia nephron sparing surgery. The target vessels of patients with hilar, single and early renal cancer are easier to find, which is more suitable for three-dimensional renal tumor vascular reconstruction technology to implement laparoscopic zero ischemia nephron sparing surgery.
Collapse
Affiliation(s)
- Song Xin
- Department of Urology, Shanghai Pudong New Area Gongli Hospital, Shanghai, 200001, China
| | - Jiang Chen
- Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200001, China.
| | - Liu Dongming
- Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200001, China
| | - Xue Wei
- Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200001, China
| | - Huang Yiran
- Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200001, China
| |
Collapse
|
3
|
Yang H, Liu H, Lin J, Xiao H, Guo Y, Mei H, Ding Q, Yuan Y, Lai X, Wu K, Wu S. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT. Eur Radiol 2024; 34:355-366. [PMID: 37528301 DOI: 10.1007/s00330-023-10016-4] [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: 04/19/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVES To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. METHODS A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature. RESULTS The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. CONCLUSIONS Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. CLINICAL RELEVANCE STATEMENT The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. KEY POINTS • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.
Collapse
Affiliation(s)
- Huancheng Yang
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Jiashan Lin
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Department of Urology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Hongwei Xiao
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yiqi Guo
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hangru Mei
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Qiuxia Ding
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Xiaohui Lai
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
| | - Song Wu
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
- Shantou University Medical College, Shantou University, Shantou, 515000, China.
- Department of Urology, Health Science Center, South China Hospital, Shenzhen University, Shenzhen, 518116, China.
| |
Collapse
|
4
|
Zhang X, Zhang J, Zhang G, Xu L, Bai X, Zhang J, Chen L, Peng Q, Jin Z, Sun H. The feasibility of contrast-enhanced CT to identify the adhesive renal venous tumor thrombus of renal cell carcinoma. Eur Radiol 2023; 33:7429-7437. [PMID: 37314475 DOI: 10.1007/s00330-023-09776-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: 02/06/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To identify adhesive renal venous tumor thrombus (RVTT) of renal cell carcinoma (RCC) by contrast-enhancement CT (CECT). MATERIALS AND METHODS Our retrospective study included 53 patients who underwent preoperative CECT and pathologically confirmed RCC combined with RVTT. They were divided into two groups based on the intra-operative findings of RVTT adhesion to the venous wall, with 26 cases in the adhesive RVTT group (ARVTT) and 27 cases in the non-adhesive group (NRVTT). The location, maximum diameter (MD) and CT values of tumors, the maximum length (ML) and width (MW) of RVTT, and length of inferior vena cava tumor thrombus were compared between the two groups. The presence of renal venous wall involvement, renal venous wall inflammation, and enlarged retroperitoneal lymph node was compared between the two groups. A receiver operating characteristic curve was used to analyze the diagnostic performance. RESULTS The MD of RCC and the ML and MW of the RVTT were all larger in the ARVTT group than in the NRVTT group (p = 0.042, p < 0.001, and p = 0.002). The proportion of renal vein wall involvement and renal vein wall inflammation were higher in the ARVTT group than in NRVTT groups (both p < 0.001). The multivariable model including ML and vascular wall inflammation to predict ARVTT could achieve the best diagnostic performance with the area under the curve, sensitivity, specificity, and accuracy of 0.91, 88.5%, 96.3%, and 92.5%, respectively. CONCLUSION The multivariable model acquired by CECT images could be used to predict RVTT adhesion. CLINICAL RELEVANCE STATEMENT For RCC patients with tumor thrombus, contrast-enhanced CT could noninvasively predict the adhesion of tumor thrombus, thus predicting the difficulty of surgery and contributing to the selection of an appropriate treatment plan. KEY POINTS • The length and width of the tumor thrombus could be used to predict its adhesion to the vessel wall. • Adhesion of the tumor thrombus can be reflected by inflammation of the renal vein wall. • The multivariable model from CECT can well predict whether the tumor thrombus adhered to the vein wall.
Collapse
Affiliation(s)
- Xiaoxiao Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Jincai Zhang
- Department of Radiology, Linyi Central Hospital, Linyi, Shandong Province, China
| | - Gumuyang Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Lili Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Xin Bai
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Jiahui Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Li Chen
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Qianyu Peng
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, Beijing, 100730, China.
| | - Hao Sun
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
- National Center for Quality Control of Radiology, Beijing, 100730, China.
| |
Collapse
|
5
|
Yang H, Wu K, Liu H, Wu P, Yuan Y, Wang L, Liu Y, Zeng H, Li J, Liu W, Wu S. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma. Eur Radiol 2023; 33:7532-7541. [PMID: 37289245 PMCID: PMC10598088 DOI: 10.1007/s00330-023-09812-9] [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: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/27/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. METHODS This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature. RESULTS In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision. CONCLUSIONS The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning. CLINICAL RELEVANCE STATEMENT We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning. KEY POINTS • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.
Collapse
Affiliation(s)
- Huancheng Yang
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Lei Wang
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yaru Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Haoyang Zeng
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Junkai Li
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Weihao Liu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Song Wu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China.
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China.
| |
Collapse
|
6
|
Ludwig DR, Thacker Y, Luo C, Narra A, Mintz AJ, Siegel CL. CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms. Clin Radiol 2023; 78:e782-e790. [PMID: 37586966 DOI: 10.1016/j.crad.2023.07.003] [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: 03/13/2023] [Revised: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
AIM To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT. MATERIALS AND METHODS Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion. RESULTS Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively. CONCLUSION A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.
Collapse
Affiliation(s)
- D R Ludwig
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.
| | - Y Thacker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C Luo
- Division of Public Health Sciences, Washington University School of Medicine, Saint Louis, MO, USA
| | - A Narra
- St George's University School of Medicine, Grenada, West Indies
| | - A J Mintz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C L Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| |
Collapse
|
7
|
Yang H, Lin J, Liu H, Yao J, Lin Q, Wang J, Jiang F, Wei L, Lin C, Wu K, Wu S. Automatic analysis framework based on 3D-CT multi-scale features for accurate prediction of Ki67 expression levels in substantial renal cell carcinoma. Insights Imaging 2023; 14:130. [PMID: 37466878 DOI: 10.1186/s13244-023-01465-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/14/2023] [Indexed: 07/20/2023] Open
Abstract
PURPOSE To investigate the effectiveness of an automatic analysis framework based on 3D-CT multi-scale features in predicting Ki67 expression levels in substantial renal cell carcinoma (RCC). METHODS This retrospective study was conducted using multi-center cohorts consisting of 588 participants with pathologically confirmed RCC. The participants were divided into an internal training set (n = 485) and an external testing set (n = 103) from four and one local hospitals, respectively. The proposed automatic analytic framework comprised a 3D kidney and tumor segmentation model constructed by 3D UNet, a 3D-CT multi-scale features extractor based on the renal-tumor feature, and a low or high Ki67 prediction classifier using XGBoost. The framework was validated using a fivefold cross-validation strategy. The Shapley additive explanation (SHAP) method was used to determine the contribution of each feature. RESULTS In the prediction of low or high Ki67, the combination of renal and tumor features achieved better performance than any single features. Internal validation using a fivefold cross-validation strategy yielded AUROC values of 0.75 ± 0.1, 0.75 ± 0.1, 0.83 ± 0.1, 0.77 ± 0.1, and 0.87 ± 0.1, respectively. The optimal model achieved an AUROC of 0.87 ± 0.1 and 0.82 ± 0.1 for low vs. high Ki67 prediction in the internal validation and external testing sets, respectively. Notably, the tumor first-order-10P was identified as the most influential feature in the model decision. CONCLUSIONS Our study suggests that the proposed automatic analysis framework based on 3D-CT multi-scale features has great potential for accurately predicting Ki67 expression levels in substantial RCC. CRITICAL RELEVANCE STATEMENT Automatic analysis framework based on 3D-CT multi-scale features provides reliable predictions for Ki67 expression levels in substantial RCC, indicating the potential usage of clinical applications.
Collapse
Affiliation(s)
- Huancheng Yang
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Jiashan Lin
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Department of Urology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Jiehua Yao
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Qianyu Lin
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Jiaxin Wang
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Feiye Jiang
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Liying Wei
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Chongyang Lin
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Kai Wu
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China.
| | - Song Wu
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China.
- Shantou University Medical College, Shantou University, Shantou, 515000, China.
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China.
| |
Collapse
|
8
|
Cheng D, Abudikeranmu Y, Tuerdi B. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram. Curr Med Imaging 2023; 19:1005-1017. [PMID: 36411581 PMCID: PMC10556396 DOI: 10.2174/1573405619666221121164235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/12/2022] [Accepted: 10/17/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE The aim of the study was to investigate the feasibility of discriminating between clear-cell renal cell carcinoma (ccRCC) and non-clear-cell renal cell carcinoma (non-ccRCC) via radiomics models and nomogram. METHODS The retrospective study included 147 patients (ccRCC=100, non-ccRCC=47) who underwent enhanced CT before surgery. CT images of the corticomedullary phase (CMP) were collected and features from the images were extracted. The data were randomly grouped into training and validation sets according to 7:3, and then the training set was normalized to extract the normalization rule for the training set, and then the rule was applied to the validation set. First, the T-test, T'-test or Wilcoxon rank-sum test were executed in the training set data to keep the statistically different parameters, and then the optimal features were picked based on the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning (ML) models were trained to differentiate ccRCC from noccRCC, rad+cli nomogram was constructed based on clinical factors and radscore (radiomics score), and the performance of the classifier was mainly measured by area under the curve (AUC), accuracy, sensitivity, specificity, and F1. Finally, the ROC curves and radar plots were plotted according to the five performance parameters. RESULTS 1130 radiomics features were extracted, there were 736 radiomics features with statistical differences were obtained, and 4 features were finally selected after the LASSO algorithm. In the validation set of this study, three of the five ML models (logistic regression, random forest and support vector machine) had excellent performance (AUC 0.9-1.0) and two models (adaptive boosting and decision tree) had good performance (AUC 0.7-0.9), all with accuracy ≥ 0.800. The rad+cli nomogram performance was found excellent in both the training set (AUC = 0.982,0.963-1.000, accuracy=0.941) and the validation set (AUC = 0.949,0.885-1.000, accuracy=0.911). The random forest model with perfect performance (AUC = 1, accuracy=1) was found superior compared to the model performance in the training set. The rad+cli nomogram model prevailed in the comparison of the model's performance in the validation set. CONCLUSION The ML models and nomogram can be used to identify the relatively common pathological subtypes in clinic and provide some reference for clinicians.
Collapse
Affiliation(s)
- Delu Cheng
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
- Department of Radiology, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, Shandong 252000, China
| | - Yeerxiati Abudikeranmu
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
| | - Batuer Tuerdi
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 83000, China
| |
Collapse
|
9
|
What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:ijms23126504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
Collapse
|
10
|
Li R, Chen W, Lu C, Li X, Chen X, Huang G, Wen Z, Li H, Tao L, Hu Y, Zhao Z, Chen Z, Ni L, Lai Y. A four-microRNA panel in serum may serve as potential biomarker for renal cell carcinoma diagnosis. Front Oncol 2022; 12:1076303. [PMID: 36727070 PMCID: PMC9885090 DOI: 10.3389/fonc.2022.1076303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/22/2022] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Renal cell carcinoma (RCC) is one out of the most universal malignant tumors globally, and its incidence is increasing annually. MicroRNA (miRNA) in serum could be considered as a non-invasive detecting biomarker for RCC diagnosis. METHOD A total of 224 participants (112 RCC patients (RCCs) and 112 normal controls (NCs)) were enrolled in the three-phrase study. Reverse transcription quantitative PCR (RT-qPCR) was applied to reveal the miRNA expression levels in RCCs and NCs. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to predict the diagnostic ability of serum miRNAs for RCC. Bioinformatic analysis and survival analysis were also included in our study. RESULTS Compared to NCs, the expression degree of miR-155-5p, miR-224-5p in serum was significantly upregulated in RCC patients, and miR-1-3p, miR-124-3p, miR-129-5p, and miR-200b-3p were downregulated. A four-miRNA panel was construed, and the AUC of the panel was 0.903 (95% CI: 0.847-0.944; p < 0.001; sensitivity = 75.61%, specificity = 93.67%). Results from GEPIA database indicated that CHL1, MPP5, and SORT1 could be seen as promising target genes of the four-miRNA panel. Survival analysis of candidate miRNAs manifested that miR-155-5p was associated with the survival rate of RCC significantly. CONCLUSIONS The four-miRNA panel in serum has a great potential to be non-invasive biomarkers for RCC sift to check.
Collapse
Affiliation(s)
- Rongkang Li
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- The Fifth Clinical Medical College of Anhui Medical University, Hefei, Anhui, China
| | - Wenkang Chen
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Chong Lu
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- The Fifth Clinical Medical College of Anhui Medical University, Hefei, Anhui, China
| | - Xinji Li
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Xuan Chen
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Guocheng Huang
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Zhenyu Wen
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Hang Li
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
| | - Lingzhi Tao
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
| | - Yimin Hu
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
| | - Zhengping Zhao
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
| | - Zebo Chen
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
| | - Liangchao Ni
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
- *Correspondence: Yongqing Lai, ; Liangchao Ni,
| | - Yongqing Lai
- Department of Urology, Guangdong and Shenzhen Key Laboratory of Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Clinical College of Anhui Medical University, Shenzhen, Guangdong, China
- The Fifth Clinical Medical College of Anhui Medical University, Hefei, Anhui, China
- *Correspondence: Yongqing Lai, ; Liangchao Ni,
| |
Collapse
|
11
|
Zhou Z. Artificial intelligence on MRI for molecular subtyping of diffuse gliomas: feature comparison, visualization, and correlation between radiomics and deep learning. Eur Radiol 2021; 32:745-746. [PMID: 34825932 DOI: 10.1007/s00330-021-08400-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/28/2021] [Accepted: 10/12/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Unit 1472, Houston, TX, 77030, USA.
| |
Collapse
|
12
|
Tang JS. Editorial comment on "Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms". Eur Radiol 2021; 32:759-760. [PMID: 34821968 DOI: 10.1007/s00330-021-08371-1] [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: 08/10/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022]
Abstract
This editorial comment refers to the article: "Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms" by Guo et al. (Eur Radiol, 2021). KEY POINTS: •Deep learning may help to uncover imaging features of autism spectrum disorder on MRI.
Collapse
Affiliation(s)
- Jennifer Sn Tang
- Department of Radiology, The Royal Melbourne Hospital, 300 Grattan Street, Melbourne, VIC, 3000, Australia.
| |
Collapse
|