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Zhang J, Liu R, Wang X, Zhang S, Shao L, Liu J, Zhao J, Wang Q, Tian J, Lu Y. Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study. J Cancer Res Clin Oncol 2024; 150:350. [PMID: 39001926 PMCID: PMC11246300 DOI: 10.1007/s00432-024-05876-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 06/29/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies greatly among individuals, how to select the optimal candidates for neoadjuvant chemoradiotherapy is crucial. This study aimed to develop an endoscopic image-based deep learning model for predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. METHODS In this multicenter observational study, pre-treatment endoscopic images of patients from two Chinese medical centers were retrospectively obtained and a deep learning-based tumor regression model was constructed. Treatment response was evaluated based on the tumor regression grade and was defined as good response and non-good response. The prediction performance of the deep learning model was evaluated in the internal and external test sets. The main outcome was the accuracy of the treatment prediction model, measured by the AUC and accuracy. RESULTS This deep learning model achieved favorable prediction performance. In the internal test set, the AUC and accuracy were 0.867 (95% CI: 0.847-0.941) and 0.836 (95% CI: 0.818-0.896), respectively. The prediction performance was fully validated in the external test set, and the model had an AUC of 0.758 (95% CI: 0.724-0.834) and an accuracy of 0.807 (95% CI: 0.774-0.843). CONCLUSION The deep learning model based on endoscopic images demonstrated exceptional predictive power for neoadjuvant treatment response, highlighting its potential for guiding personalized therapy.
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Affiliation(s)
- Junhao Zhang
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China
| | - Ruiqing Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China
| | - Xujian Wang
- Graduate School for Elite Engineers, Shandong University, Jinan, China
| | - Shiwei Zhang
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Junheng Liu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiahui Zhao
- Department of Gastroenterology, Endoscopy Center, The First Hospital of Jilin University, Changchun, China
| | - Quan Wang
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Shinan District, Qingdao, 266003, China.
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Hu T, Gong J, Sun Y, Li M, Cai C, Li X, Cui Y, Zhang X, Tong T. Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study. MedComm (Beijing) 2024; 5:e609. [PMID: 38911065 PMCID: PMC11190348 DOI: 10.1002/mco2.609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 04/18/2024] [Accepted: 05/16/2024] [Indexed: 06/25/2024] Open
Abstract
Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.
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Affiliation(s)
- TingDan Hu
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jing Gong
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - YiQun Sun
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - MengLei Li
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - ChongPeng Cai
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - XinXiang Li
- Department of Colorectal SurgeryFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - YanFen Cui
- Department of RadiologyShanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical UniversityTaiyuanChina
| | - XiaoYan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing)Department of Radiology, Peking University Cancer Hospital and InstituteBeijingChina
| | - Tong Tong
- Department of RadiologyFudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan UniversityShanghaiChina
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Horvat N, Papanikolaou N, Koh DM. Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use. Radiol Artif Intell 2024; 6:e230437. [PMID: 38717290 PMCID: PMC11294952 DOI: 10.1148/ryai.230437] [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: 10/08/2023] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.
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Affiliation(s)
- Natally Horvat
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Nikolaos Papanikolaou
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Dow-Mu Koh
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
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He J, Wang SX, Liu P. Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis. Br J Radiol 2024; 97:1243-1254. [PMID: 38730550 PMCID: PMC11186567 DOI: 10.1093/bjr/tqae098] [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: 09/13/2023] [Revised: 01/15/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVES To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging. METHODS We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. RESULTS A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries. CONCLUSIONS This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC. ADVANCES IN KNOWLEDGE Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.
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Affiliation(s)
- Jia He
- Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People’s Hospital, Changsha 410002, China
| | | | - Peng Liu
- Department of Radiology, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People’s Hospital, Changsha 410002, China
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Shen H, Jin Z, Chen Q, Zhang L, You J, Zhang S, Zhang B. Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:598-614. [PMID: 38512622 DOI: 10.1007/s11547-024-01796-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/24/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.
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Affiliation(s)
- Hui Shen
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.
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Qin Q, Gan X, Lin P, Pang J, Gao R, Wen R, Liu D, Tang Q, Liu C, He Y, Yang H, Wu Y. Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. BMC Med Imaging 2024; 24:65. [PMID: 38500022 PMCID: PMC10946192 DOI: 10.1186/s12880-024-01237-0] [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: 08/11/2023] [Accepted: 03/02/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. MATERIALS AND METHODS This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. RESULTS The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. CONCLUSION The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.
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Affiliation(s)
- Qiong Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Xiangyu Gan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Jingshu Pang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Dun Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Quanquan Tang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Changwen Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
| | - Yuquan Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
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Xia S, Li Q, Zhu HT, Zhang XY, Shi YJ, Yang D, Wu J, Guan Z, Lu Q, Li XT, Sun YS. Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework. BMC Cancer 2024; 24:315. [PMID: 38454349 PMCID: PMC10919051 DOI: 10.1186/s12885-024-11997-1] [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: 05/31/2023] [Accepted: 02/13/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.
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Affiliation(s)
- Shaojun Xia
- Institute of Medical Technology, Peking University Health Science Center, Haidian District, No. 38 Xueyuan Road, Beijing, 100191, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Qingyang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Hai-Tao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Xiao-Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Yan-Jie Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Ding Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Jiaqi Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Zhen Guan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Qiaoyuan Lu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China
| | - Ying-Shi Sun
- Institute of Medical Technology, Peking University Health Science Center, Haidian District, No. 38 Xueyuan Road, Beijing, 100191, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Hai Dian District, No. 52 Fu Cheng Road, Beijing, 100142, China.
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Kim M, Park T, Oh BY, Kim MJ, Cho BJ, Son IT. Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review. Ann Coloproctol 2024; 40:13-26. [PMID: 38414120 PMCID: PMC10915525 DOI: 10.3393/ac.2023.00892.0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
PURPOSE The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat-ment strategies for patients with rectal cancer. METHODS This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation. RESULTS AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer-ing noninvasive and cost-effective alternatives for identifying genetic mutations. CONCLUSION Image-based AI studies for rectal can-cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.
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Affiliation(s)
- Minsung Kim
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Taeyong Park
- Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang, Korea
| | - Bo Young Oh
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Min Jeong Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Bum-Joo Cho
- Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang, Korea
| | - Il Tae Son
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
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Zhou Y, Zhang J, Li C, Chen J, Lv F, Deng Y, Chen S, Du Y, Li F. Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters. Biomed Eng Online 2023; 22:123. [PMID: 38093245 PMCID: PMC10717163 DOI: 10.1186/s12938-023-01182-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 11/23/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning. MATERIALS AND METHODS This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively. RESULTS A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit. CONCLUSIONS The radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.
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Affiliation(s)
- Ye Zhou
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Jinwei Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Chenghai Li
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
| | - Jinyun Chen
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yongbin Deng
- Chongqing Haifu Hospital, Chongqing, 401121, China
| | - Siyao Chen
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Yuling Du
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China
| | - Faqi Li
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.
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10
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Xiang Y, Li S, Song M, Wang H, Hu K, Wang F, Wang Z, Niu Z, Liu J, Cai Y, Li Y, Zhu X, Geng J, Zhang Y, Teng H, Wang W. KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients. BMC Med Imaging 2023; 23:210. [PMID: 38087207 PMCID: PMC10717608 DOI: 10.1186/s12880-023-01173-5] [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: 08/02/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis. METHODS In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test. RESULTS Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p = 0.016, IDI: 1.08%, p = 0.687; NRI 2.23%, p = 0.766). CONCLUSIONS We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.
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Affiliation(s)
- Yirong Xiang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Shuai Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Maxiaowei Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Hongzhi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Ke Hu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fengwei Wang
- Department of Oncology, Tianjin Union Medical Center, Tianjin, China
| | - Zhi Wang
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Zhiyong Niu
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Jin Liu
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | - Yong Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yongheng Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Xianggao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Jianhao Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yangzi Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Huajing Teng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China.
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 DOI: 10.1007/s12029-022-00909-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] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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12
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Hassaninejad H, Abdollahi H, Abedi I, Amouheidari A, Tavakoli MB. Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison. Phys Eng Sci Med 2023; 46:1353-1363. [PMID: 37556091 DOI: 10.1007/s13246-023-01260-5] [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: 11/22/2022] [Accepted: 04/13/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients' information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity. METHODS Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance. RESULTS The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%. CONCLUSIONS This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.
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Affiliation(s)
- Hossein Hassaninejad
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Iraj Abedi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | | | - Mohamad Bagher Tavakoli
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
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Lee J, Yoo SK, Kim K, Lee BM, Park VY, Kim JS, Kim YB. Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncol Lett 2023; 26:422. [PMID: 37664669 PMCID: PMC10472028 DOI: 10.3892/ol.2023.14008] [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: 05/03/2023] [Accepted: 07/19/2023] [Indexed: 09/05/2023] Open
Abstract
Locoregional recurrence (LRR) is the predominant pattern of relapse after definitive breast cancer treatment. The present study aimed to develop machine learning (ML)-based radiomics models to predict LRR in patients with breast cancer by using preoperative magnetic resonance imaging (MRI) data. Data from patients with localized breast cancer that underwent preoperative MRI between January 2013 and December 2017 were collected. Propensity score matching (PSM) was performed to adjust for clinical factors between patients with and without LRR. Radiomics features were obtained from T2-weighted with and without fat-suppressed MRI and contrast-enhanced T1-weighted with fat-suppressed MRI. In the present study five ML models were designed, three base models (support vector machine, random forest, and logistic regression) and two ensemble models (voting model and stacking model) composed of the three base models, and the performance of each base model was compared with the stacking model. After PSM, 28 patients with LRR and 86 patients without LRR were included. Of these 114 patients, 80 patients were randomly selected to train the models, and the remaining 34 patients were used to evaluate the performance of the trained models. In total, 5,064 features were obtained from each patient, and 47-51 features were selected by applying variance threshold and least absolute shrinkage and selection operator. The stacking model demonstrated superior performance in area under the receiver operating characteristic curve (AUC), with an AUC of 0.78 compared to a range of 0.61 to 0.70 for the other models. An external validation study to investigate the efficacy of the stacking model of the present study was initiated and is still ongoing (Korean Radiation Oncology Group 2206).
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Affiliation(s)
- Joongyo Lee
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 06273, Republic of Korea
| | - Sang Kyun Yoo
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Kangpyo Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Yonsei University Health System, Seoul 06351, Republic of Korea
| | - Byung Min Lee
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Yonsei University Health System, Uijeongbu, Gyeonggi 11765, Republic of Korea
| | - Vivian Youngjean Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Heavy Ion Therapy Research Institute, Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Republic of Korea
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Kimura C, Crowder SE, Kin C. Is It Really Gone? Assessing Response to Neoadjuvant Therapy in Rectal Cancer. J Gastrointest Cancer 2023; 54:703-711. [PMID: 36417142 DOI: 10.1007/s12029-022-00889-x] [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] [Accepted: 11/13/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Non-operative management of rectal cancer is a feasible and appealing treatment option for patients who develop a complete response after neoadjuvant therapy. However, identifying patients who are complete responders is often a challenge. This review aims to present and discuss current evidence and recommendations regarding the assessment of treatment response in rectal cancer. METHODS A review of the current literature on rectal cancer restaging was performed. Studies included in this review explored the optimal interval between the end of neoadjuvant therapy and restaging, as well as modalities of assessment and their diagnostic performance. RESULTS The current standard for restaging rectal cancer is a multimodal assessment with the digital rectal examination, endoscopy, and T2-weighted MRI with diffusion-weighted imaging. Other diagnostic procedures under investigation are PET/MRI, radiomics, confocal laser endomicroscopy, artificial intelligence-assisted endoscopy, cell-free DNA, and prediction models incorporating one or more of the above-mentioned exams. CONCLUSION Non-operative management of rectal cancer requires a multidisciplinary approach. Understanding of the robustness and limitations of each exam is critical to inform patient selection for that treatment strategy.
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Affiliation(s)
- Cintia Kimura
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA
| | - Sarah Elizabeth Crowder
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA
- Brigham Young University, Provo, UT, USA
| | - Cindy Kin
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA.
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15
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Peng W, Wan L, Wang S, Zou S, Zhao X, Zhang H. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol 2023; 13:1234619. [PMID: 37664046 PMCID: PMC10468971 DOI: 10.3389/fonc.2023.1234619] [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: 06/05/2023] [Accepted: 06/30/2023] [Indexed: 09/05/2023] Open
Abstract
Objective Radiomics based on magnetic resonance imaging (MRI) shows potential for prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC); however, thorough comparison between radiomics and traditional models is deficient. We aimed to construct multiple-time-scale (pretreatment, posttreatment, and combined) radiomic models to predict pathological complete response (pCR) and compare their utility to those of traditional clinical models. Methods In this research, 165 LARC patients undergoing nCRT followed by surgery were enrolled retrospectively, which were divided into training and testing sets in the ratio of 7:3. Morphological features on pre- and posttreatment MRI, coupled with clinical data, were evaluated by univariable and multivariable logistic regression analysis for constructing clinical models. Radiomic parameters were derived from pre- and posttreatment T2- and diffusion-weighted images to develop the radiomic signatures. The clinical-radiomics models were then generated. All the models were developed in the training set and then tested in the testing set, the performance of which was assessed using the area under the receiver operating characteristic curve (AUC). Radiomic models were compared with the clinical models with the DeLong test. Results One hundred and sixty-five patients (median age, 55 years; age interquartile range, 47-62 years; 116 males) were enrolled in the study. The pretreatment maximum tumor length, posttreatment maximum tumor length, and magnetic resonance tumor regression grade were selected as independent predictors for pCR in the clinical models. In the testing set, the pre- and posttreatment and combined clinical models generated AUCs of 0.625, 0.842, and 0.842 for predicting pCR, respectively. The MRI-based radiomic models performed reasonably well in predicting pCR, but neither the pure radiomic signatures (AUCs, 0.734, 0.817, and 0.801 for the pre- and posttreatment and combined radiomic signatures, respectively) nor the clinical-radiomics models (AUCs, 0.734, 0.860, and 0.801 for the pre- and posttreatment and combined clinical-radiomics models, respectively) showed significant added value compared with the clinical models (all P > 0.05). Conclusion The MRI-based radiomic models exhibited no definite added value compared with the clinical models for predicting pCR in LARC. Radiomic models can serve as ancillary tools for tailoring adequate treatment strategies.
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Affiliation(s)
- Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Department of Pharmaceutical Diagnosis, GE Healthcare, Life Sciences, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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16
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Fang Z, Pu H, Chen XL, Yuan Y, Zhang F, Li H. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer. Abdom Radiol (NY) 2023; 48:2270-2283. [PMID: 37085730 DOI: 10.1007/s00261-023-03910-4] [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: 01/13/2023] [Revised: 04/01/2023] [Accepted: 04/05/2023] [Indexed: 04/23/2023]
Abstract
PURPOSE To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up. RESULTS Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030). CONCLUSION Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.
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Affiliation(s)
- Zhu Fang
- 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
| | - 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
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, 55#Four Section of South Renmin Road, Wuhou District, Chengdu, 610000, China
| | - Yi Yuan
- 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
| | - Feng Zhang
- 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
| | - 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.
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Borelli B, Germani MM, Carullo M, Mattioni R, Manfredi B, Sainato A, Rossi P, Vagli P, Balestri R, Buccianti P, Morelli L, Antoniotti C, Cremolini C, Masi G, Moretto R. Total neoadjuvant treatment and organ preservation strategies in the management of localized rectal cancer: a narrative review and evidence-based algorithm. Crit Rev Oncol Hematol 2023; 186:103985. [PMID: 37059274 DOI: 10.1016/j.critrevonc.2023.103985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/26/2023] [Accepted: 04/11/2023] [Indexed: 04/16/2023] Open
Abstract
The multimodal approach with total mesorectal excision preceded by neoadjuvant (chemo)radiotherapy represented the mainstay treatment for locally advanced rectal cancer (LARC) for a long time. However, the benefit of adjuvant chemotherapy in terms of distant relapse reduction is limited. Recently, chemotherapy regimens administered before surgery and incorporated with (chemo)radiotherapy in total neoadjuvant treatment protocols have been established as new options in the management of LARC. Meanwhile, patients with clinical complete response to neoadjuvant treatment can benefit from organ preservation strategies, aimed at sparing surgery and long-term post-operative morbidities, while preserving an adequate disease control. However, the introduction of a non-operative management in clinical practice is a matter of debate with some concerns regarding the risk of local recurrence and long-term outcomes. In this review, we discuss how these recent advances are reshaping the multimodal management of localized rectal cancer and propose an algorithm to place them in the clinical practice.
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Affiliation(s)
- Beatrice Borelli
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Marco Maria Germani
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Martina Carullo
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberto Mattioni
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Bruno Manfredi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Aldo Sainato
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Piercarlo Rossi
- Diagnostic and Interventional Radiology Unit, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Paola Vagli
- Diagnostic and Interventional Radiology Unit, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Riccardo Balestri
- General Surgery, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Piero Buccianti
- General Surgery, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Luca Morelli
- General Surgery, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Carlotta Antoniotti
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Chiara Cremolini
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Gianluca Masi
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberto Moretto
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.
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18
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Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L, Hu S, Wu Y, Deng K, Yang W, Liu X. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 2023; 33:1906-1917. [PMID: 36355199 DOI: 10.1007/s00330-022-09204-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/20/2022] [Accepted: 09/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Zeli Chen
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yehuan Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Shaowei Hu
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kan Deng
- Clinical Science, Philips Healthcare, Guangzhou, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
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19
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Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time? Cancers (Basel) 2023; 15:cancers15020432. [PMID: 36672381 PMCID: PMC9857080 DOI: 10.3390/cancers15020432] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/12/2023] Open
Abstract
In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.
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20
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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21
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MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study. Clin Transl Radiat Oncol 2022; 38:175-182. [DOI: 10.1016/j.ctro.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
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22
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Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP, Huang G. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1026216. [PMID: 36313696 PMCID: PMC9597310 DOI: 10.3389/fonc.2022.1026216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.
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Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Qing-Yong Zheng
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jin-Hui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Di-Liang He
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Jian-Xin Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Lian-Ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Gang Huang,
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23
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Pham TT, Lim S, Lin M. Predicting neoadjuvant chemoradiotherapy response with functional imaging and liquid biomarkers in locally advanced rectal cancer. Expert Rev Anticancer Ther 2022; 22:1081-1098. [PMID: 35993178 DOI: 10.1080/14737140.2022.2114457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
INTRODUCTION Non-invasive predictive quantitative biomarkers are required to guide treatment individualization in patients with locally advanced rectal cancer (LARC) in order to maximise therapeutic outcomes and minimise treatment toxicity. Magnetic resonance imaging (MRI), positron emission tomography (PET) and blood biomarkers have the potential to predict chemoradiotherapy (CRT) response in LARC. AREAS COVERED This review examines the value of functional imaging (MRI and PET) and liquid biomarkers (circulating tumor cells (CTCs) and circulating tumor nucleic acid (ctNA)) in the prediction of CRT response in LARC. Selected imaging and liquid biomarker studies are presented and the current status of the most promising imaging (apparent diffusion co-efficient (ADC), Ktrans, SUVmax, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and liquid biomarkers (circulating tumor cells (CTCs), circulating tumor nucleic acid (ctNA)) is discussed. The potential applications of imaging and liquid biomarkers for treatment stratification and a pathway to clinical translation are presented. EXPERT OPINION Functional imaging and liquid biomarkers provide novel ways of predicting CRT response. The clinical and technical validation of the most promising imaging and liquid biopsy biomarkers in multi-centre studies with harmonised acquisition techniques is required. This will enable clinical trials to investigate treatment escalation or de-escalation pathways in rectal cancer.
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Affiliation(s)
- Trang Thanh Pham
- South West Sydney Clinical School, Faculty of Medicine and Health, University of New South Wales, Liverpool NSW Australia 2170.,Department of Radiation Oncology, Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool NSW Australia 2170.,Ingham Institute for Applied Medical Research, Liverpool NSW Australia 2170
| | - Stephanie Lim
- Ingham Institute for Applied Medical Research, Liverpool NSW Australia 2170.,Department of Medical Oncology, Macarthur Cancer Therapy Centre, Campbelltown Hospital, Campbelltown Australia 2560.,School of Medicine, Western Sydney University, Campbelltown, Sydney 2560
| | - Michael Lin
- South West Sydney Clinical School, Faculty of Medicine and Health, University of New South Wales, Liverpool NSW Australia 2170.,School of Medicine, Western Sydney University, Campbelltown, Sydney 2560.,Department of Nuclear Medicine, Liverpool Hospital, Liverpool NSW Australia 2170
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24
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Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study. Abdom Radiol (NY) 2022; 47:2770-2782. [PMID: 35710951 PMCID: PMC10150388 DOI: 10.1007/s00261-022-03572-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. METHODS Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. RESULTS Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%-97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70-0.89 vs k = 0.25, 95% CI 0.11-0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). CONCLUSION We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC.
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25
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Panic J, Defeudis A, Mazzetti S, Rosati S, Giannetto G, Micilotta M, Vassallo L, Gatti M, Regge D, Balestra G, Giannini V. A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:5066-5069. [PMID: 36086406 DOI: 10.1109/embc48229.2022.9871326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.
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26
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Shahzadi I, Zwanenburg A, Lattermann A, Linge A, Baldus C, Peeken JC, Combs SE, Diefenhardt M, Rödel C, Kirste S, Grosu AL, Baumann M, Krause M, Troost EGC, Löck S. Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Sci Rep 2022; 12:10192. [PMID: 35715462 PMCID: PMC9205935 DOI: 10.1038/s41598-022-13967-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/17/2022] [Indexed: 11/21/2022] Open
Abstract
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.
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Affiliation(s)
- Iram Shahzadi
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alex Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Annika Lattermann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Annett Linge
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Baldus
- Department of Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jan C Peeken
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Stephanie E Combs
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Markus Diefenhardt
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Claus Rödel
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anca-Ligia Grosu
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Baumann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mechthild Krause
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Esther G C Troost
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Steffen Löck
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. .,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
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Chen BY, Xie H, Li Y, Jiang XH, Xiong L, Tang XF, Lin XF, Li L, Cai PQ. MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study. Front Oncol 2022; 12:801743. [PMID: 35646677 PMCID: PMC9133669 DOI: 10.3389/fonc.2022.801743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/31/2022] [Indexed: 12/22/2022] Open
Abstract
This is a prospective, single center study aimed to evaluate the predictive power of peritumor and intratumor radiomics features assessed using T2 weight image (T2WI) of baseline magnetic resonance imaging (MRI) in evaluating pathological good response to NAC in patients with LARC (including Tany N+ or T3/4a Nany but not T4b). In total, 137 patients with LARC received NAC between April 2014 and August 2020. All patients were undergoing contrast-enhanced MRI and 129 patients contained small field of view (sFOV) sequence which were performed prior to treatment. The tumor regression grade standard was based on pathological response. The training and validation sets (n=91 vs. n=46) were established by random allocation of the patients. Receiver operating characteristic curve (ROC) analysis was applied to estimate the performance of different models based on clinical characteristics and radiomics features obtained from MRI, including peritumor and intratumor features, in predicting treatment response; these effects were calculated using the area under the curve (AUC). The performance and agreement of the nomogram were estimated using calibration plots. In total, 24 patients (17.52%) achieved a complete or near-complete response. For the individual radiomics model in the validation set, the performance of peritumor radiomics model in predicting treatment response yield an AUC of 0.838, while that of intratumor radiomics model is 0.805, which show no statically significant difference between then(P>0.05). The traditional and selective clinical features model shows a poor predictive ability in treatment response (AUC=0.596 and 0.521) in validation set. The AUC of combined radiomics model was improved compared to that of the individual radiomics models in the validation sets (AUC=0.844). The combined clinic-radiomics model yield the highest AUC (0.871) in the validation set, although it did not improve the performance of the radiomics model for predicting treatment response statically (P>0.05). Good agreement and discrimination were observed in the nomogram predictions. Both peritumor and intratumor radiomics features performed similarly in predicting a good response to NAC in patients with LARC. The clinic-radiomics model showed the best performance in predicting treatment response.
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Affiliation(s)
- Bi-Yun Chen
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hui Xie
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuan Li
- Department of Colorectal, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xin-Hua Jiang
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Lang Xiong
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao-Feng Tang
- Department of Ultrasound, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao-Feng Lin
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Li
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Pei-Qiang Cai
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
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28
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Defeudis A, Mazzetti S, Panic J, Micilotta M, Vassallo L, Giannetto G, Gatti M, Faletti R, Cirillo S, Regge D, Giannini V. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur Radiol Exp 2022; 6:19. [PMID: 35501512 PMCID: PMC9061921 DOI: 10.1186/s41747-022-00272-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/23/2022] [Indexed: 12/29/2022] Open
Abstract
Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00272-2.
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Affiliation(s)
- Arianna Defeudis
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy. .,Department of Surgical Sciences, University of Turin, Turin, Italy. .,Radiology Unit, SS Annunziata Savigliano Hospital, Cuneo, Italy.
| | - Simone Mazzetti
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Jovana Panic
- Department of Surgical Sciences, University of Turin, Turin, Italy.,Politecnico di Torino, Electronic and Telecommunication Department (DET), Turin, Italy
| | | | - Lorenzo Vassallo
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Giuliana Giannetto
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | | | - Daniele Regge
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Valentina Giannini
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
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Filitto G, Coppola F, Curti N, Giampieri E, Dall’Olio D, Merlotti A, Cattabriga A, Cocozza MA, Taninokuchi Tomassoni M, Remondini D, Pierotti L, Strigari L, Cuicchi D, Guido A, Rihawi K, D’Errico A, Di Fabio F, Poggioli G, Morganti AG, Ricciardiello L, Golfieri R, Castellani G. Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer. Cancers (Basel) 2022; 14:cancers14092231. [PMID: 35565360 PMCID: PMC9100060 DOI: 10.3390/cancers14092231] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. Abstract Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant’Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.
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Affiliation(s)
- Giuseppe Filitto
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy; (G.F.); (G.C.)
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, 40138 Bologna, Italy
| | - Nico Curti
- eDIMES Lab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- INFN Bologna, 40127 Bologna, Italy;
- Correspondence: (N.C.); (E.G.)
| | - Enrico Giampieri
- eDIMES Lab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (E.G.)
| | - Daniele Dall’Olio
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Alessandra Merlotti
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Makoto Taninokuchi Tomassoni
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Daniel Remondini
- INFN Bologna, 40127 Bologna, Italy;
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Luisa Pierotti
- Sant’Orsola-Malpighi Polyclinic, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Lidia Strigari
- Department of Medical Physics, Sant’Orsola-Malpighi Polyclinic, IRCCS Azienda Ospedaliero-Universitaria di Bologn, 40138 Bologna, Italy;
| | - Dajana Cuicchi
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Alessandra Guido
- Department of Radiation Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Karim Rihawi
- Division of Medical Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (K.R.); (F.D.F.)
| | - Antonietta D’Errico
- Pathology Unit, Department of Specialized, Experimental and Diagnostic Medicine, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Francesca Di Fabio
- Division of Medical Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (K.R.); (F.D.F.)
| | - Gilberto Poggioli
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Alessio Giuseppe Morganti
- Department of Radiation Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Luigi Ricciardiello
- Department of Medical and Surgical Science, University of Bologna, 40138 Bologna, Italy;
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy; (G.F.); (G.C.)
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Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer. Radiat Oncol 2022; 17:84. [PMID: 35484597 PMCID: PMC9052564 DOI: 10.1186/s13014-022-02053-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/11/2022] [Indexed: 02/08/2023] Open
Abstract
Background To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum. Methods Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.
Results Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).
Conclusions Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.
Supplementary Information The online version contains supplementary material available at 10.1186/s13014-022-02053-y.
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MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer. Br J Cancer 2022; 127:249-257. [PMID: 35368044 PMCID: PMC9296479 DOI: 10.1038/s41416-022-01786-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 01/29/2022] [Accepted: 03/08/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).
Methods
Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.
Results
We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.
Conclusions
The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.
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Tomita H, Kobayashi T, Takaya E, Mishiro S, Hirahara D, Fujikawa A, Kurihara Y, Mimura H, Kobayashi Y. Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study. Eur Radiol 2022; 32:5353-5361. [PMID: 35201406 DOI: 10.1007/s00330-022-08630-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/15/2022] [Accepted: 02/02/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy. METHODS Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables. RESULTS The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWIintra). The log-rank test showed that DWIintra was significantly associated with PFS (p = 0.013). DWIintra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023). CONCLUSION DL models using DWIintra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment. KEY POINTS • Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.
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Affiliation(s)
- Hayato Tomita
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
| | - Tatsuaki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Eichi Takaya
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan
| | - Sono Mishiro
- Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan
| | - Daisuke Hirahara
- Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan
| | - Atsuko Fujikawa
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yoshiko Kurihara
- Department of Radiology, Machida Municipal Hospital, 2-15-41 Asahi-cho, Machida, Tokyo, 194-0023, Japan
| | - Hidefumi Mimura
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yasuyuki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
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Wan L, Sun Z, Peng W, Wang S, Li J, Zhao Q, Wang S, Ouyang H, Zhao X, Zou S, Zhang H. Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics. J Magn Reson Imaging 2022; 56:1130-1142. [PMID: 35142001 DOI: 10.1002/jmri.28108] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness. PURPOSE To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT. STUDY TYPE Retrospective. POPULATION A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC. FIELD STRENGTH/SEQUENCE A 3.0-T; fast spin echo T2 -weighted and single-shot EPI diffusion-weighted images. ASSESSMENT The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model. STATISTICAL TESTS The χ2 test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference. RESULTS Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups. DATA CONCLUSION A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Zhuo Sun
- Thorough Images, Chaoyang District, Beijing, China
| | - Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Sicong Wang
- Department of Pharmaceutical Diagnosis, GE Healthcare, Life Sciences, Beijing, China
| | - Jiangtao Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Qing Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Shuhao Wang
- Thorough Images, Chaoyang District, Beijing, China
| | - Han Ouyang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
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Liu W, Li Y, Zhang X, Li J, Sun J, Lv H, Wang Z. Preoperative T and N Restaging of Rectal Cancer After Neoadjuvant Chemoradiotherapy: An Accuracy Comparison Between MSCT and MRI. Front Oncol 2022; 11:806749. [PMID: 35127518 PMCID: PMC8813750 DOI: 10.3389/fonc.2021.806749] [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: 11/01/2021] [Accepted: 12/27/2021] [Indexed: 11/17/2022] Open
Abstract
Background It is well established that magnetic resonance imaging (MRI) is better than multi-slice computed tomography (MSCT) for the accurate diagnosis of pretreatment tumor (T) and node (N) staging of rectal cancer. However, the diagnostic value of MRI and MSCT in local restaging of rectal cancer after neoadjuvant chemoradiotherapy (NCRT) is controversial. The aim of this study is to investigate the performance of the two imaging exams in restaging of patients with rectal cancer. Methods Patients with rectal cancer from April 2015 to April 2021 were analyzed retrospectively. The inclusion criteria are as follows: 1) diagnosis of rectal cancer through pathology; 2) NCRT had been performed; 3) all patients had undergone both MSCT and MRI examination before the surgery. Exclusion criteria are as follows: 1) incomplete clinical and imaging data; 2) previous history of pelvic surgery. Two radiologists performed T and N staging of patient images. Diagnostic accuracy, consistency analysis, and error restaging distribution of the two imaging exams for T and N restaging of rectal cancer were assessed using postoperative pathological staging as the gold standard. Results A total of 62 patients (49 men; mean age: 59 years; age range 29–83 years) were included in the study. The diagnostic accuracy of MSCT and MRI for T restaging was 51.6% (95% CI 39.3%–63.9%) and 41.9% (95% CI 29.6%–54.2%), respectively, and no statistical difference was found between them (p > 0.05). The diagnostic accuracy of MSCT and MRI for N restaging was 56.5% (95% CI 44.2%–68.8%) and 53.2% (95% CI 40.8%–65.6%), respectively, and no statistical difference was found between them (p > 0.05). The consistency analysis showed that T restaging (κ = 0.583, p < 0.001) and N restaging (κ = 0.644, p < 0.001) were similar between MSCT and MRI. There was no significant difference in the distribution of over, accurate, or low staging in T restaging (p > 0.05) and N restaging (p > 0.05) between MSCT and MRI. Conclusions MSCT and MRI have similarly poor performance in the diagnosis of preoperative T and N restaging of rectal cancer after NCRT. Neither of them cannot effectively stage the ypT0-1 of rectal cancer. These findings may be of clinical relevance for planning less imaging exam.
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Affiliation(s)
- Wenjuan Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yuyi Li
- Department of Anorectal Surgery, Jining No. 1 People's Hospital, Jining, China
| | - Xue Zhang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Huang G, Cui Y, Wang P, Ren J, Wang L, Ma Y, Jia Y, Ma X, Zhao L. Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion. Front Oncol 2022; 11:663370. [PMID: 35096556 PMCID: PMC8790703 DOI: 10.3389/fonc.2021.663370] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 12/17/2021] [Indexed: 01/03/2023] Open
Abstract
Background Detection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable. Objective To train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies. Materials and Methods The model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set. Results Feature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set. Conclusion Biomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.
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Affiliation(s)
- Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yaqiong Cui
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China.,The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China
| | - Ping Wang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | | | - Lili Wang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yaqiong Ma
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yingmei Jia
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Xiaomei Ma
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Lianping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
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Miranda J, Tan GXV, Fernandes MC, Yildirim O, Sims JA, de Arimateia Batista Araujo-Filho J, Machado FADM, Assuncao AN, Nomura CH, Horvat N. Rectal MRI radiomics for predicting pathological complete response: Where we are. Clin Imaging 2022; 82:141-149. [PMID: 34826772 PMCID: PMC9119743 DOI: 10.1016/j.clinimag.2021.10.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/21/2021] [Accepted: 10/11/2021] [Indexed: 02/03/2023]
Abstract
Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Diagnosticos da America SA (DASA), Sao Paulo, SP, Brazil
| | - Gary Xia Vern Tan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John A. Sims
- Department of Biomedical Engineering, Universidade Federal do ABC, Santo Andre, SP, Brazil
| | | | | | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Hospital Sirio-Libanes, Sao Paulo, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost. Int J Colorectal Dis 2022; 37:1621-1634. [PMID: 35704090 PMCID: PMC9262764 DOI: 10.1007/s00384-022-04157-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/17/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Watch and wait strategy is a safe and effective alternative to surgery in patients with locally advanced rectal cancer (LARC) who have achieved pathological complete response (pCR) after neoadjuvant therapy (NAT); present restaging methods do not meet clinical needs. This study aimed to construct a machine learning (ML) model to predict pCR preoperatively. METHODS LARC patients who received NAT were included to generate an extreme gradient boosting-based ML model to predict pCR. The group was divided into a training set and a tuning set at a 7:3 ratio. The SHapley Additive exPlanations value was used to quantify feature importance. The ML model was compared with a nomogram model developed using independent risk factors identified by conventional multivariate logistic regression analysis. RESULTS Compared with the nomogram model, our ML model improved the area under the receiver operating characteristics from 0.72 to 0.95, sensitivity from 43 to 82.2%, and specificity from 87.1 to 91.6% in the training set, the same trend applied to the tuning set. Neoadjuvant radiotherapy, preoperative carbohydrate antigen 125 (CA125), CA199, carcinoembryonic antigen level, and depth of tumor invasion were significant in predicting pCR in both models. CONCLUSION Our ML model is a potential alternative to the existing assessment tools to conduct triage treatment for patients and provides reference for clinicians in tailoring individual treatment: the watch and wait strategy is used to avoid surgical trauma in pCR patients, and non-pCR patients receive surgical treatment to avoid missing the optimal operation time window.
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Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 2021; 4:e8-e17. [PMID: 34952679 DOI: 10.1016/s2589-7500(21)00215-6] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/15/2021] [Accepted: 09/01/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. METHODS In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). FINDINGS Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001). INTERPRETATION RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer. FUNDING National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.
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Zhang S, Yu M, Chen D, Li P, Tang B, Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol Rep 2021; 47:34. [PMID: 34935061 PMCID: PMC8717123 DOI: 10.3892/or.2021.8245] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.
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Affiliation(s)
- Siyu Zhang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Mingrong Yu
- College of Physical Education, Sichuan Agricultural University, Ya'an, Sichuan 625000, P.R. China
| | - Dan Chen
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Peidong Li
- Second Department of Gastrointestinal Surgery, The Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, P.R. China
| | - Bin Tang
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
| | - Jie Li
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
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40
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Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. CURRENT COLORECTAL CANCER REPORTS 2021. [DOI: 10.1007/s11888-021-00470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D, Barbaro B, Corvari B, Dinapoli N, Giraffa M, Meldolesi E, Manfredi R, Valentini V, Gambacorta MA. Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol Med 2021; 127:11-20. [PMID: 34725772 DOI: 10.1007/s11547-021-01421-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Paola de Franco
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy.
| | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Carano
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Barbara Corvari
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Nicola Dinapoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Martina Giraffa
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
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Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27:5306-5321. [PMID: 34539134 PMCID: PMC8409167 DOI: 10.3748/wjg.v27.i32.5306] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesca Boccadifuoco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
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Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
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Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - É Deutsch
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
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Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging. Radiother Oncol 2021; 161:183-190. [PMID: 34139211 DOI: 10.1016/j.radonc.2021.06.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging. MATERIALS AND METHODS A total of 466 patients with locally advanced rectal cancer who received preoperative chemoradiotherapy followed by surgical resection were collected from single center, among whom 113 (24.3%) were allocated to the holdout testing set. Complete response (pCR) was defined as Dworak tumor regression grade (TRG) 4, while good response (GR) was defined as TRG 3 or 4. Based on post-chemoradiotherapy T2-weighted axial MR images, two deep learning models were developed to predict pCR and GR, respectively. The prediction performance of the deep learning models was evaluated in the testing set and was compared to that of a senior radiologist and a radiation oncologist. RESULTS The deep learning model showed an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.76, 0.30, 0.96, 0.67, 0.87, and 85.0% for predicting pCR and 0.72, 0.54, 0.81, 0.60, 0.77, and 71.7% for predicting GR, respectively. The deep learning model had a superior predictive performance than the observers. Fair agreement between the ground truth and the model was shown for pCR prediction (kappa = 0.34) and GR prediction (kappa = 0.36). CONCLUSIONS The post-chemoradiotherapy T2-weighted axial MR image-based deep learning model showed acceptable performance in predicting pCR or GR in patients with rectal cancer, compared with human observers.
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Zhuang Z, Liu Z, Li J, Wang X, Xie P, Xiong F, Hu J, Meng X, Huang M, Deng Y, Lan P, Yu H, Luo Y. Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer. J Transl Med 2021; 19:256. [PMID: 34112180 PMCID: PMC8194221 DOI: 10.1186/s12967-021-02919-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/31/2021] [Indexed: 01/06/2023] Open
Abstract
Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02919-x.
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Affiliation(s)
- Zhuokai Zhuang
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Zongchao Liu
- Department of Biostatistics, Columbia University, New York, NY, 10032, USA
| | - Juan Li
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Xiaolin Wang
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Peiyi Xie
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Fei Xiong
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Jiancong Hu
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Xiaochun Meng
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Meijin Huang
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Yanhong Deng
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Department of Medical Oncology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Ping Lan
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Huichuan Yu
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.
| | - Yanxin Luo
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.
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Coppola F, Mottola M, Lo Monaco S, Cattabriga A, Cocozza MA, Yuan JC, De Benedittis C, Cuicchi D, Guido A, Rojas Llimpe FL, D’Errico A, Ardizzoni A, Poggioli G, Strigari L, Morganti AG, Bazzoli F, Ricciardiello L, Golfieri R, Bevilacqua A. The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Diagnostics (Basel) 2021; 11:diagnostics11050795. [PMID: 33924854 PMCID: PMC8146691 DOI: 10.3390/diagnostics11050795] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 12/12/2022] Open
Abstract
Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10−5; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Via della Signora 2, 20122 Milan, Italy
| | - Margherita Mottola
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, 40125 Bologna, Italy; (M.M.); (A.B.)
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
- Correspondence:
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
| | - Jia Cheng Yuan
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
| | - Caterina De Benedittis
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
| | - Dajana Cuicchi
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Alessandra Guido
- Department of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Fabiola Lorena Rojas Llimpe
- Division of Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.L.R.L.); (A.A.)
| | - Antonietta D’Errico
- Pathology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy;
| | - Andrea Ardizzoni
- Division of Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.L.R.L.); (A.A.)
| | - Gilberto Poggioli
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, S. Orsola-Malpighi Hospital, 40138 Bologna, Italy;
| | - Alessio Giuseppe Morganti
- Department of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Franco Bazzoli
- Department of Medical and Surgical Sciences, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (F.B.); (L.R.)
| | - Luigi Ricciardiello
- Department of Medical and Surgical Sciences, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, 40138 Bologna, Italy; (F.B.); (L.R.)
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (J.C.Y.); (C.D.B.); (R.G.)
| | - Alessandro Bevilacqua
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, 40125 Bologna, Italy; (M.M.); (A.B.)
- Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
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Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics (Basel) 2021; 11:diagnostics11050756. [PMID: 33922483 PMCID: PMC8146913 DOI: 10.3390/diagnostics11050756] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 12/24/2022] Open
Abstract
While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.
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Wesdorp NJ, van Goor VJ, Kemna R, Jansma EP, van Waesberghe JHTM, Swijnenburg RJ, Punt CJA, Huiskens J, Kazemier G. Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature. Surg Oncol 2021; 38:101578. [PMID: 33866191 DOI: 10.1016/j.suronc.2021.101578] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND To better select patients with colorectal liver metastases (CRLM) for an optimal selection of treatment strategy (i.e. local, systemic or combined treatment) new prognostic models are warranted. In the last decade, radiomics has emerged as a field to create predictive models based on imaging features. This systematic review aims to investigate the current state and potential of radiomics to predict clinical outcomes in patients with CRLM. METHODS A comprehensive literature search was conducted in the electronic databases of PubMed, Embase, and Cochrane Library, according to PRISMA guidelines. Original studies reporting on radiomics predicting clinical outcome in patients diagnosed with CRLM were included. Clinical outcomes were defined as response to systemic treatment, recurrence of disease, and survival (overall, progression-free, disease-free). Primary outcome was the predictive performance of radiomics. A narrative synthesis of the results was made. Methodological quality was assessed using the radiomics quality score. RESULTS In 11 out of 14 included studies, radiomics was predictive for response to treatment, recurrence of disease, survival, or a combination of outcomes. Combining clinical parameters and radiomic features in multivariate modelling often improved the predictive performance. Different types of individual features were found prognostic. Noticeable were the contrary levels of heterogeneous and homogeneous features in patients with good response. The methodological quality as assessed by the radiomics quality score varied considerably between studies. CONCLUSION Radiomics appears a promising non-invasive method to predict clinical outcome and improve personalized decision-making in patients with CRLM. However, results were contradictory and difficult to compare. Standardized prospective studies are warranted to establish the added value of radiomics in patients with CRLM.
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Affiliation(s)
- N J Wesdorp
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.
| | - V J van Goor
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - R Kemna
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - E P Jansma
- Amsterdam UMC, University of Amsterdam, Department of Epidemiology and Biostatistics, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - J H T M van Waesberghe
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Molecular Imaging, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - R J Swijnenburg
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
| | - C J A Punt
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Department of Epidemiology, Utrecht, the Netherlands
| | - J Huiskens
- SAS Institute B.V., Flevolaan 69, Huizen, the Netherlands
| | - G Kazemier
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
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Liu Y, Zhang FJ, Zhao XX, Yang Y, Liang CY, Feng LL, Wan XB, Ding Y, Zhang YW. Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer. Cancer Manag Res 2021; 13:3235-3246. [PMID: 33880066 PMCID: PMC8053518 DOI: 10.2147/cmar.s295317] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/18/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC). However, the accuracy of traditional clinical indicators in predicting tumor response is poor. Recently, radiomics based on magnetic resonance imaging (MRI) has been regarded as a promising noninvasive assessment method. The present study was conducted to develop a model to predict the pathological response by analyzing the quantitative features of MRI and clinical risk factors, which might predict the therapeutic effects in patients with LARC as accurately as possible before treatment. Patients and Methods A total of 82 patients with LARC were enrolled as the training cohort and internal validation cohort. The pre-CRT MRI after pretreatment was acquired to extract texture features, which was finally selected through the minimum redundancy maximum relevance (mRMR) algorithm. A support vector machine (SVM) was used as a classifier to classify different tumor responses. A joint radiomics model combined with clinical risk factors was then developed and evaluated by receiver operating characteristic (ROC) curves. External validation was performed with 107 patients from another center to evaluate the applicability of the model. Results Twenty top image texture features were extracted from 6192 extracted-radiomic features. The radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI) and contrast-enhanced T1-weighted imaging (T1+C) demonstrated an area under the curve (AUC) of 0.8910 (0.8114–0.9706) and 0.8938 (0.8084–0.9792), respectively. The AUC value rose to 0.9371 (0.8751–0.9997) and 0.9113 (0.8449–0.9776), respectively, when the circumferential resection margin (CRM) and carbohydrate antigen 19-9 (CA19-9) levels were incorporated. Clinical usefulness was confirmed in an external validation cohort as well (AUC, 0.6413 and 0.6818). Conclusion Our study indicated that the joint radiomics prediction model combined with clinical risk factors showed good predictive ability regarding the treatment response of tumors as accurately as possible before treatment.
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Affiliation(s)
- Yang Liu
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Feng-Jiao Zhang
- Shanghai Concord Medical Cancer Center, Shanghai, 200001, People's Republic of China
| | - Xi-Xi Zhao
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yuan Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Chun-Yi Liang
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Li-Li Feng
- Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Xiang-Bo Wan
- Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Yi Ding
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yao-Wei Zhang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
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Yan M, Wang W. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Sci Prog 2021; 104:36850421997295. [PMID: 33687294 PMCID: PMC10453712 DOI: 10.1177/0036850421997295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy.This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance metrics of logistic regression include accuracy, precision, the receiver operating characteristic curves, and recall.The combination features of clinical variables and radiomics can predict the tumor volume change after radiotherapy with 88.7% accuracy (88.6% precision, 88.7% recall, and 88.7% ROC area).Radiomics features combined with medical knowledge have a great potential to predict accurately tumor volume change of stage III NSCLC after radiotherapy with 60 Gy.
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Affiliation(s)
- Mengmeng Yan
- Urban Vocational College of Sichuan, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
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