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Sun Z, Xia F, Lv W, Li J, Zou Y, Wu J. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer. Am J Surg 2024; 232:59-67. [PMID: 38272767 DOI: 10.1016/j.amjsurg.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/17/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
AIM Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively. CONCLUSION The radiomics nomogram based on Rad-score (T2WI + ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.
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Affiliation(s)
- Zhen Sun
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Feng Xia
- Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Jin Li
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - You Zou
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jianhong Wu
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Han W, Wang Y, Li T, Dong Y, Dang Y, He L, Xu L, Zhou Y, Li Y, Wang X. A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer. PeerJ 2024; 12:e17254. [PMID: 38685941 PMCID: PMC11057426 DOI: 10.7717/peerj.17254] [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: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 05/02/2024] Open
Abstract
Background Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.
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Affiliation(s)
- Wei Han
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yingshu Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Tao Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuke Dong
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yanwei Dang
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Liang He
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Lianfang Xu
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuhao Zhou
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yujie Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xudong Wang
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Li H, Chai L, Pu H, Yin LL, Li M, Zhang X, Liu YS, Pang MH, Lu T. T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer. Insights Imaging 2024; 15:57. [PMID: 38411722 PMCID: PMC10899552 DOI: 10.1186/s13244-024-01625-8] [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: 06/25/2023] [Accepted: 01/18/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. METHODS One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). RESULTS Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233-5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247-5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). CONCLUSION The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. CRITICAL RELEVANCE STATEMENT A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. KEY POINTS • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Li Chai
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Pu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Long-Lin Yin
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
- Institute of Radiation Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Mou Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Beijing, 100176, China
| | - Yi-Sha Liu
- Department of Pathology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Ming-Hui Pang
- Department of Geriatric Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Tao Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China.
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Jin Y, Yin H, Zhang H, Wang Y, Liu S, Yang L, Song B. Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features. Insights Imaging 2023; 14:221. [PMID: 38117396 PMCID: PMC10733230 DOI: 10.1186/s13244-023-01564-w] [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: 06/15/2023] [Accepted: 11/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients. METHODS AND METHODS A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA). RESULTS The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674-0.788), 0.710 (95% CI, 0.649-0.766), 0.767 (95% CI, 0.710-0.819), and 0.857 (95% CI, 0.807-0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities. CONCLUSIONS The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. CRITICAL RELEVANCE STATEMENT The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. KEY POINTS • Preoperative non-invasive identification of TDs is of great clinical significance. • The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. • A preoperative nomogram provides gastroenterologist with an accurate and effective tool.
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Affiliation(s)
- Yumei Jin
- Department of Medical Imaging Center, Qujing First People's Hospital, Qujing, 655000, Yunnan Province, China.
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
| | - Hongkun Yin
- Beijing Infervision Technology Co.Ltd, Beijing, China
| | - Huiling Zhang
- Beijing Infervision Technology Co.Ltd, Beijing, China
| | - Yewu Wang
- Department of Joint and Sports Medicine, Qujing First People's Hospital, Qujing, 655000, Yunnan Province, China
| | - Shengmei Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Ling Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan Province, 572000, China.
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Feng F, Liu Y, Bao J, Hong R, Hu S, Hu C. Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer. Abdom Radiol (NY) 2023; 48:3310-3321. [PMID: 37578553 DOI: 10.1007/s00261-023-04013-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 07/05/2023] [Accepted: 07/19/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE To establish and validate an integrated model incorporating multiregional magnetic resonance imaging (MRI) radiomics features and clinical factors to predict tumor deposits (TDs) preoperatively in resectable rectal cancer (RC). METHODS This study retrospectively included 148 resectable RC patients [TDs+ (n = 45); TDs- (n = 103)] from August 2016 to August 2022, who were divided randomly into a testing cohort (n = 45) and a training cohort (n = 103). Radiomics features were extracted from the volume of interest on T2-weighted images (T2WI) and diffusion-weighted images (DWI) from pretreatment MRI. Model construction was performed after feature selection. Finally, five classification models were developed by support vector machine (SVM) algorithm to predict TDs in resectable RC using the selected clinical factor, single-regional radiomics features (extracted from primary tumor), and multiregional radiomics features (extracted from the primary tumor and mesorectal fat). Receiver-operating characteristic (ROC) curve analysis was employed to assess the discrimination performance of the five models. The AUCs of five models were compared by DeLon's test. RESULTS The training and testing cohorts included 31 (30.1%) and 14 (31.1%) patients with TDs, respectively. The AUCs of multiregional radiomics, single-regional radiomics, and the clinical models for predicting TDs were 0.839, 0.765, and 0.793, respectively. An integrated model incorporating multiregional radiomics features and clinical factors showed good predictive performance for predicting TDs in resectable RC (AUC, 0.931; 95% CI, 0.841-0.988), which demonstrated superiority over clinical model (P = 0.016), the single-regional radiomics model (P = 0.042), and the multiregional radiomics model (P = 0.025). CONCLUSION An integrated model combining multiregional MRI radiomic features and clinical factors can improve prediction performance for TDs and guide clinicians in implementing treatment plans individually for resectable RC patients.
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Affiliation(s)
- Feiwen Feng
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China
| | - Yuanqing Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China
| | - Jiayi Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China
| | - Rong Hong
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China.
- Institute of Medical Imaging, Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China.
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China.
- Institute of Medical Imaging, Soochow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu Province, China.
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Li H, Chen XL, Liu H, Liu YS, Li ZL, Pang MH, Pu H. MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study. Eur Radiol 2023; 33:7561-7572. [PMID: 37160427 DOI: 10.1007/s00330-023-09723-9] [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: 12/07/2022] [Revised: 03/07/2023] [Accepted: 03/20/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer. MATERIALS AND METHODS A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. RESULTS For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)-based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively. CONCLUSION Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk. CLINICAL RELEVANCE STATEMENT Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery. KEY POINTS • Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, 610000, China
| | | | - Yi-Sha Liu
- Department of Pathology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Zhen-Lin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ming-Hui Pang
- Department of Gastrointestinal Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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Vural Topuz Ö, Aksu A, Yılmaz Özgüven MB. A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings. Rev Esp Med Nucl Imagen Mol 2023; 42:359-366. [PMID: 37088299 DOI: 10.1016/j.remnie.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 04/25/2023]
Abstract
OBJECTIVE We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC). METHODS Our retrospective study included 77 CRC patients who had preoperative 18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance. RESULTS A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB. CONCLUSIONS Intratumoral and peritumoral radiomics derived from 18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC.
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Affiliation(s)
- Özge Vural Topuz
- University of Health Sciences, Başakşehir Cam and Sakura City Hospital, Department of Nuclear Medicine, Istanbul, Turkey.
| | - Ayşegül Aksu
- İzmir Katip Çelebi University, Atatürk Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey
| | - Müveddet Banu Yılmaz Özgüven
- University of Health Sciences, Başakşehir Cam and Sakura City Hospital, Department of Pathology, Istanbul, Turkey
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Li M, Xu G, Chen Q, Xue T, Peng H, Wang Y, Shi H, Duan S, Feng F. Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study. Acad Radiol 2023; 30:1572-1583. [PMID: 36566155 DOI: 10.1016/j.acra.2022.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a computed tomography (CT)-based radiomics nomogram for the preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients with colon cancer. MATERIALS AND METHODS This retrospective study included 383 consecutive patients with colon cancer from two centers. Radiomics features were extracted from portal venous phase CT images. Least absolute shrinkage and selection operator regression was applied for feature selection and radiomics signature construction. The multivariate logistic regression model was used to establish a radiomics nomogram. The performance of the nomogram was assessed by using receiver operating characteristic curves, calibration curves and decision curve analysis. Kaplan‒Meier survival analysis was used to assess the difference of the overall survival (OS) in the TDs-positive and TDs-negative groups. RESULTS The radiomics signature was composed of 11 TDs status related features. The AUCs of the radiomics model in the training cohort, internal validation and external validation cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated the radiomics signature and clinical independent predictors (CT-N, CEA and CA199) showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the training cohort, internal validation and external validation cohorts, respectively. The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor of OS. CONCLUSION The radiomics nomogram based on CT radiomics features and clinical independent predictors could effectively predict the preoperative TDs status and OS of colon cancer. IMPORTANT FINDINGS CT-based radiomics nomogram may be applied in the individual preoperative prediction of TDs status in colon cancer. Additionally, there was a significant difference in OS between the high-risk and low-risk groups defined by the radiomics nomogram, in which patients with high-risk TDs had a significantly worse OS, compared with those with low-risk TDs.
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Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Guodong Xu
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Yuwei Wang
- Department of Record room, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Hui Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361.
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9
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Yang R, Zhao H, Wang X, Ding Z, Tao Y, Zhang C, Zhou Y. Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer. ABDOMINAL RADIOLOGY (NEW YORK) 2023; 48:1268-1279. [PMID: 36750477 DOI: 10.1007/s00261-023-03825-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 02/09/2023]
Abstract
PURPOSE To develop a magnetic resonance imaging radiomics model to predict tumor deposits (TDs) and prognosis in stage T3 lymph node positive (T3N+) rectal cancer (RC). METHODS This retrospective study included 163 patients with pathologically confirmed T3N + RC from December 2013 to December 2015. The patients were divided into two groups for training and testing. Extracting radiomic features from MR images and selecting features using principal component analysis (PCA), then radiomic scores (rad-scores) were obtained by logistic regression analysis. Finally, a combined TDs prediction model containing rad-scores and clinical features was developed. A receiver operating characteristic (ROC) curve was used to assess the prediction performance. The overall survival (OS) rate in patients with high-risk and low-risk TDs predicted by rad-scores was validated by Kaplan-Meier survival curves. RESULTS Of the 163 patients included, histological TDs was diagnosed in 45 patients. The area under the curve (AUC) of the final model was 0.833 (training) and 0.844 (testing). The patients with rad-scores predicted high-risk were associated with OS. In addition, postoperative adjuvant therapy improved the OS of the high-risk TDs group (P < 0.05). CONCLUSION MRI-based radiomics modeling helps in the preoperative prediction of patients with TDs+ in T3N + RC and provides risk stratification for neoadjuvant therapy. In addition, the rad-scores of TDs could suggest different survival benefits of postoperative adjuvant therapy for T3N + RC patients and guide clinical treatment.
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Affiliation(s)
- Rui Yang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Hongxin Zhao
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150010, Heilongjiang, China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150010, Heilongjiang, China
| | - Zhipeng Ding
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150010, Heilongjiang, China
| | - Yuqing Tao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Chunhui Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
| | - Yang Zhou
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150010, Heilongjiang, China.
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10
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Fu C, Shao T, Hou M, Qu J, Li P, Yang Z, Shan K, Wu M, Li W, Wang X, Zhang J, Luo F, Zhou L, Sun J, Zhao F. Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models. Front Oncol 2023; 13:1078863. [PMID: 36890815 PMCID: PMC9986582 DOI: 10.3389/fonc.2023.1078863] [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: 10/24/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Background This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.
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Affiliation(s)
- Chunlong Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Tingting Shao
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Qu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ping Li
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, China
| | - Zebin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Kangfei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Meikang Wu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Weida Li
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xuan Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingfeng Zhang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo, China
| | - Fanghong Luo
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Long Zhou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo, China.,Cancer Center, Zhejiang University, Hangzhou, China
| | - Fenhua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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Li M, Gu H, Xue T, Peng H, Chen Q, Zhu X, Duan S, Feng F. CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study. Br J Radiol 2023; 96:20220568. [PMID: 36318241 PMCID: PMC10997017 DOI: 10.1259/bjr.20220568] [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: 06/01/2022] [Revised: 09/20/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC). METHODS 357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor. RESULTS The rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively. CONCLUSION Both 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance. ADVANCES IN KNOWLEDGE This is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.
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Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hongmei Gu
- Department of Radiology, Affiliated Hospital of Nantong
University, Nantong, PR China
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Xinghua Zhu
- Department of Pathology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
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