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Wagner S, Ewald C, Freitag D, Herrmann KH, Koch A, Bauer J, Vogl TJ, Kemmling A, Gufler H. Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI. J Neurooncol 2024; 169:257-267. [PMID: 38960965 PMCID: PMC11341603 DOI: 10.1007/s11060-024-04725-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 05/25/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.
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Affiliation(s)
- Sabine Wagner
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany.
- Department of Neuroradiology, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany.
| | - Christian Ewald
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Diana Freitag
- Department of Neurosurgery, Section of Experimental Neurooncology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07743, Jena, Germany
| | - Arend Koch
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Berlin Institute of Health, Charité University Medicine, 10117, Berlin, Germany
| | - Johannes Bauer
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
| | - André Kemmling
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany
| | - Hubert Gufler
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
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Lo HZ, Choy KT, Kong JCH. FDG-PET/MRI in colorectal cancer care: an updated systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04460-z. [PMID: 39073608 DOI: 10.1007/s00261-024-04460-z] [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: 04/23/2024] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE Since its introduction in 2011, FDG-PET/MRI has been advocated as a useful adjunct in colorectal cancer care. However, gaps and limitations in current research remain. This systematic review aims to review the current literature to quantify the utility of FDG-PET/MRI in colorectal cancer care. METHODS An up-to-date review was performed on the available literature between 2000 and 2023 on PubMed, EMBASE, Medline, databases. All studies reporting on the use of FDG-PET/MRI in colorectal cancer care were analyzed. The main outcome measures were accuracy in initial staging, restaging, and detection of metastatic disease in both rectal as well as colon cancers. The secondary outcome was comparing the performance of FDG-PET/MRI versus Standard of Care Imaging (SCI). Finally, the clinical significance of FDG-PET/MRI was measured in the change in management resulting from imaging findings. RESULTS A total of 22 observational studies were included, accounting for 988 patients. When individually compared to current Standard of Care Imaging (SCI)-MRI pelvis for rectal cancer and thoraco-abdominal contrast CT, PET/MRI proved superior in terms of distant metastatic disease detection. This led to as much as 21.0% change in management. However, the technological limitations of PET/MRI were once again highlighted, suggesting SCI should retain its place as first-line imaging. CONCLUSION FDG-PET/MRI appears to be a promising adjunct in staging and restaging of colorectal cancer in carefully selected patients.
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Affiliation(s)
- Hui Zhen Lo
- School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Kay Tai Choy
- Department of Surgery, Austin Health, Melbourne, VIC, Australia
| | - Joseph Cherng Huei Kong
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
- Department of Colorectal Surgery, Alfred Hospital, Melbourne, VIC, Australia
- Central Clinical School, Monash University, Melbourne, VIC, Australia
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Liu J, Sui C, Bian H, Li Y, Wang Z, Fu J, Qi L, Chen K, Xu W, Li X. Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer. Front Oncol 2024; 14:1425837. [PMID: 39132503 PMCID: PMC11310012 DOI: 10.3389/fonc.2024.1425837] [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: 04/30/2024] [Accepted: 07/09/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). Methods A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. Conclusions The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
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Affiliation(s)
- Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Haiman Bian
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yue Li
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jie Fu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Lisha Qi
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 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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Liang H, Ma D, Ma Y, Hang Y, Guan Z, Zhang Y, Wei Y, Wang P, Zhang M. Comparison of conventional MRI analysis versus MRI-based radiomics to predict the circumferential margin resection involvement of rectal cancer. BMC Gastroenterol 2024; 24:209. [PMID: 38902675 PMCID: PMC11191295 DOI: 10.1186/s12876-024-03274-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 05/22/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND To compare the application of conventional MRI analysis and MRI-based radiomics to identify the circumferential resection margin (CRM) status of rectal cancer (RC). METHODS A cohort of 301 RC patients with 66 CRM invloved status and 235 CRM non-involved status were enrolled in this retrospective study between September 2017 and August 2022. Conventional MRI characteristics included gender, age, diameter, distance to anus, MRI-based T/N phase, CEA, and CA 19 - 9, then the relevant logistic model (Logistic-cMRI) was built. MRI-based radiomics of rectal cancer and mesorectal fascia were calculated after volume of interest segmentation, and the logistic model of rectal cancer radiomics (Logistic-rcRadio) and mesorectal fascia radiomics (Logistic-mfRadio) were constructed. And the combined nomogram (nomo-cMRI/rcRadio/mfRadio) containing conventional MRI characteristics, radiomics of rectal cancer and mesorectal fascia was developed. The receiver operator characteristic curve (ROC) was delineated and the area under curve (AUC) was calculated the efficiency of models. RESULTS The AUC of Logistic-cMRI was 0.864 (95%CI, 0.820 to 0.901). The AUC of Logistic-rcRadio was 0.883 (95%CI, 0.832 to 0.928) in the training set and 0.725 (95%CI, 0.616 to 0.826) in the testing set. The AUCs of Logistic-mfRadio was 0.891 (95%CI, 0.838 to 0.936) in the training set and 0.820 (95%CI, 0.725 to 0.905) in the testing set. The AUCs of nomo-cMRI/rcRadio/mfRadio were the highest in both the training set of 0.942 (95%CI, 0.901 to 0.969) and the testing set of 0.909 (95%CI, 0.830 to 0.959). CONCLUSION MRI-based radiomics of rectal cancer and mesorectal fascia showed similar efficacy in predicting the CRM status of RC. The combined nomogram performed better in assessment.
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Affiliation(s)
- Hong Liang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, China
- School of Medical Imaging, Hangzhou Medical College, No.481, Binwen Road, Hangzhou, 310000, China
| | - Dongnan Ma
- Yangming College of Ningbo University, Ningbo, China
| | - Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yuan Hang
- Department of Colorectal Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zheng Guan
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yang Zhang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yuguo Wei
- GE Healthcare, Precision Health Institution, Hangzhou, China
| | - Peng Wang
- Department of Radiology, 411 Hospital of Shanghai University, No.15, Dongjiangwan Road, Shanghai, 200080, China.
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, China.
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Yang X, Gao C, Sun N, Qin X, Liu X, Zhang C. An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer. Front Oncol 2024; 14:1353780. [PMID: 38846980 PMCID: PMC11153703 DOI: 10.3389/fonc.2024.1353780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
Objective The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery. Materials and methods A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature. Results Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model. Conclusion We established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.
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Affiliation(s)
- Xianyue Yang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Chuanfen Gao
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Nian Sun
- Department of Ultrasound, Anhui Provincial Maternity and Child Health Hospital, Hefei, Anhui, China
| | - Xiachuan Qin
- Department of Ultrasound, Nanchong Central Hospital (Beijing Anzhen Hospital Nanchong Hospital), The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Xiaoling Liu
- Department of Ultrasound, Nanchong Central Hospital (Beijing Anzhen Hospital Nanchong Hospital), The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 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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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [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: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Li Y, Zeng C, Du Y. Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients. Medicine (Baltimore) 2023; 102:e36004. [PMID: 37960749 PMCID: PMC10637426 DOI: 10.1097/md.0000000000036004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/17/2023] [Indexed: 11/15/2023] Open
Abstract
Rectal cancer is the eighth most prevalent malignancy worldwide with a 3.2% mortality rate and 3.9% incidence rate. Radiologists still have difficulty in correctly diagnosing lymph node metastases that have been suspected preoperatively. To assess the effectiveness of a model combining clinical and radiomics features for the preoperative prediction of lymph node metastasis in rectal cancer. We retrospectively analyzed data from 104 patients with rectal cancer. All patients were selected as samples for the training (n = 72) and validation cohorts (n = 32). Lymph nodes (LNs) in diffusion-weighted images were analyzed to obtain 842 radiomic characteristics, which were then used to draw the region of interest. Logistic regression, least absolute shrinkage and selection operator, and between-group and within-group correlation analyses were combined to establish the radiomic score (rad-score). Receiver operating characteristic curves were used to estimate the prediction accuracy of the model. A calibration curve was constructed to test the predictive ability of the model. A decision curve analysis was performed to analyze the model's value in clinical application. The area under the curve for the radiomics-clinical, clinical, and radiomics models was 0.856, 0.810, and 0.781, respectively, in the training cohort and 0.880, 0.849, and 0.827, respectively, in the validation cohort. The calibration curve and DCA showed that the radiomics-clinical prediction model had good prediction accuracy, which was higher than that of the other models. The radiomics-clinical model showed a favorable predictive performance for the preoperative prediction of LN metastasis in patients with rectal cancer.
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Affiliation(s)
- Yehan Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, China
- Department of Radiology, Chongqing Cancer Hospital, Chongqing, China
| | - Chen Zeng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, China
- Department of Radiology, West China Hospital of Sichuan University, Sichuan, China
| | - Yong Du
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, China
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10
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Zhou X, Yu Y, Feng Y, Ding G, Liu P, Liu L, Ren W, Zhu Y, Cao W. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiat Oncol 2023; 18:175. [PMID: 37891611 PMCID: PMC10612200 DOI: 10.1186/s13014-023-02352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/13/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC. METHODS This retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital. RESULTS The deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism. CONCLUSIONS The self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.
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Affiliation(s)
- Xuezhi Zhou
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yi Yu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yanru Feng
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Guojun Ding
- Department of Radiology, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Luying Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Wenjie Ren
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China.
| | - Yuan Zhu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China.
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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11
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Li C, Chen H, Zhang B, Fang Y, Sun W, Wu D, Su Z, Shen L, Wei Q. Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Cancers (Basel) 2023; 15:5134. [PMID: 37958309 PMCID: PMC10648149 DOI: 10.3390/cancers15215134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/15/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.
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Affiliation(s)
- Chao Li
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Haiyan Chen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Bicheng Zhang
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Yimin Fang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China;
| | - Wenzheng Sun
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Dang Wu
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Zhuo Su
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Li Shen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Qichun Wei
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
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12
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Wei MY, Arafat Y, Lee M, Kosmider S, Loft M, Faragher I, Gibbs P, Yeung JM. Emerging trends in the prediction of pathological tumour response in rectal cancer following neoadjuvant therapy. ANZ J Surg 2023; 93:2285-2286. [PMID: 36716258 DOI: 10.1111/ans.18303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/10/2023] [Accepted: 01/21/2023] [Indexed: 02/01/2023]
Affiliation(s)
- Matthew Y Wei
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Yasser Arafat
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Margaret Lee
- Department of Medical Oncology, Western Health, Melbourne, Victoria, Australia
| | - Suzanne Kosmider
- Department of Medical Oncology, Western Health, Melbourne, Victoria, Australia
| | - Matthew Loft
- Department of Medical Oncology, Western Health, Melbourne, Victoria, Australia
| | - Ian Faragher
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
| | - Peter Gibbs
- Department of Medical Oncology, Western Health, Melbourne, Victoria, Australia
| | - Justin M Yeung
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
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13
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Wen L, Liu J, Hu P, Bi F, Liu S, Jian L, Zhu S, Nie S, Cao F, Lu Q, Yu X, Liu K. MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Acad Radiol 2023; 30 Suppl 1:S176-S184. [PMID: 36739228 DOI: 10.1016/j.acra.2022.12.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/13/2022] [Accepted: 12/21/2022] [Indexed: 02/05/2023]
Abstract
RATIONALE AND OBJECTIVES The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment. MATERIALS AND METHODS A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machine-learning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test. RESULTS The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and post-nCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05). CONCLUSIONS The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.
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Affiliation(s)
- Lu Wen
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Jun Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China.
| | - Pingsheng Hu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Feng Bi
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China.
| | - Siye Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Lian Jian
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Suyu Zhu
- Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, P.R. China
| | - Shaolin Nie
- Department of Colorectal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Fang Cao
- Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, P.R. China
| | - Ke Liu
- Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, P.R. China.
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14
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Zheng YM, Che JY, Yuan MG, Wu ZJ, Pang J, Zhou RZ, Li XL, Dong C. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma. Acad Radiol 2023; 30:1591-1599. [PMID: 36460582 DOI: 10.1016/j.acra.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC. MATERIALS AND METHODS A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA). RESULTS Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC. CONCLUSION A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jun-Yi Che
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui-Zhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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15
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Kaanders JHAM, Bussink J, Aarntzen EHJG, Braam P, Rütten H, van der Maazen RWM, Verheij M, van den Bosch S. [18F]FDG-PET-Based Personalized Radiotherapy Dose Prescription. Semin Radiat Oncol 2023; 33:287-297. [PMID: 37331783 DOI: 10.1016/j.semradonc.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
PET imaging with 2'-deoxy-2'-[18F]fluoro-D-glucose ([18F]FDG) has become one of the pillars in the management of malignant diseases. It has proven value in diagnostic workup, treatment policy, follow-up, and as prognosticator for outcome. [18F]FDG is widely available and standards have been developed for PET acquisition protocols and quantitative analyses. More recently, [18F]FDG-PET is also starting to be appreciated as a decision aid for treatment personalization. This review focuses on the potential of [18F]FDG-PET for individualized radiotherapy dose prescription. This includes dose painting, gradient dose prescription, and [18F]FDG-PET guided response-adapted dose prescription. The current status, progress, and future expectations of these developments for various tumor types are discussed.
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Affiliation(s)
- Johannes H A M Kaanders
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands..
| | - Johan Bussink
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands
| | - Erik H J G Aarntzen
- Department of Medical Imaging, Radboud university medical center, Nijmegen, The Netherlands
| | - Pètra Braam
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands
| | - Heidi Rütten
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands
| | | | - Marcel Verheij
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands
| | - Sven van den Bosch
- Department of Radiation Oncology, Radboud university medical center, Nijmegen, The Netherlands
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Vuijk FA, Feshtali Shahbazi S, Noortman WA, van Velden FH, Dibbets-Schneider P, Marinelli AW, Neijenhuis PA, Schmitz R, Ghariq E, Velema LA, Peters FP, Smit F, Peeters KC, Temmink SJ, Crobach SA, Putter H, Vahrmeijer AL, Hilling DE, de Geus-Oei LF. Baseline and early digital [ 18 F]FDG PET/CT and multiparametric MRI contain promising features to predict response to neoadjuvant therapy in locally advanced rectal cancer patients: a pilot study. Nucl Med Commun 2023; 44:613-621. [PMID: 37132268 PMCID: PMC10246883 DOI: 10.1097/mnm.0000000000001703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/28/2023] [Indexed: 05/04/2023]
Abstract
OBJECTIVE In this pilot study, we investigated the feasibility of response prediction using digital [ 18 F]FDG PET/computed tomography (CT) and multiparametric MRI before, during, and after neoadjuvant chemoradiation therapy in locally advanced rectal cancer (LARC) patients and aimed to select the most promising imaging modalities and timepoints for further investigation in a larger trial. METHODS Rectal cancer patients scheduled to undergo neoadjuvant chemoradiation therapy were prospectively included in this trial, and underwent multiparametric MRI and [ 18 F]FDG PET/CT before, 2 weeks into, and 6-8 weeks after chemoradiation therapy. Two groups were created based on pathological tumor regression grade, that is, good responders (TRG1-2) and poor responders (TRG3-5). Using binary logistic regression analysis with a cutoff value of P ≤ 0.2, promising predictive features for response were selected. RESULTS Nineteen patients were included. Of these, 5 were good responders, and 14 were poor responders. Patient characteristics of these groups were similar at baseline. Fifty-seven features were extracted, of which 13 were found to be promising predictors of response. Baseline [T2: volume, diffusion-weighted imaging (DWI): apparent diffusion coefficient (ADC) mean, DWI: difference entropy], early response (T2: volume change, DWI: ADC mean change) and end-of-treatment presurgical evaluation MRI (T2: gray level nonuniformity, DWI: inverse difference normalized, DWI: gray level nonuniformity normalized), as well as baseline (metabolic tumor volume, total lesion glycolysis) and early response PET/CT (Δ maximum standardized uptake value, Δ peak standardized uptake value corrected for lean body mass), were promising features. CONCLUSION Both multiparametric MRI and [ 18 F]FDG PET/CT contain promising imaging features to predict response to neoadjuvant chemoradiotherapy in LARC patients. A future larger trial should investigate baseline, early response, and end-of-treatment presurgical evaluation MRI and baseline and early response PET/CT.
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Affiliation(s)
| | | | - Wyanne A. Noortman
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center
- Biomedical Photonic Imaging Group, University of Twente, Enschede
| | | | | | | | | | | | - Eidrees Ghariq
- Department of Radiology, Leiden University Medical Center, Leiden
| | - Laura A. Velema
- Department of Radiation Oncology, Leiden University Medical Center
| | - Femke P. Peters
- Department of Radiation Oncology, Leiden University Medical Center
- Department of Radiation Oncology, Antoni van Leeuwenhoek Hospital, Amsterdam
| | - Frits Smit
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center
| | | | | | | | - Hein Putter
- Department of Medical Statistics, Leiden University Medical Center, Leiden
| | | | - Denise E. Hilling
- Department of Surgery, Leiden University Medical Center
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam
- Department of Surgery, Ijsselland Ziekenhuis, Capelle a/d IJssel
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center
- Biomedical Photonic Imaging Group, University of Twente, Enschede
- Department of Radiation Science & Technology, Technical University Delft, The Netherlands
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17
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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18
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Chen K, Hou L, Chen M, Li S, Shi Y, Raynor WY, Yang H. Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics. Life (Basel) 2023; 13:life13040884. [PMID: 37109413 PMCID: PMC10142286 DOI: 10.3390/life13040884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/18/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.
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Affiliation(s)
- Kuifei Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Meng Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Shuling Li
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Yangyang Shi
- Department of Radiation Oncology, University of Arizona, Tucson, AZ 85724, USA
| | - William Y. Raynor
- Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Haihua Yang
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
- Correspondence: or
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19
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Zhuang F, Haoran E, Huang J, Wu J, Xu L, Zhang L, Li Q, Li C, Zhao Y, Yang M, Ma M, She Y, Chen H, Luo Q, Zhao D, Chen C. Utility of 18F-FDG PET/CT uptake values in predicting response to neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer. Lung Cancer 2023; 178:20-27. [PMID: 36764154 DOI: 10.1016/j.lungcan.2023.02.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Reliable predictive markers are lacking for resectable non-small cell lung cancer (NSCLC) patients treated with neoadjuvant chemoimmunotherapy. The present study investigated the utility of SUVmax values acquired from PET/CT to predict the response to neoadjuvant chemoimmunotherapy for resectable NSCLC. MATERAL AND METHODS SUVmax, clinical and pathological outcomes, were collected from patients in 5 hospitals. Patients who received dynamic PET/CT surveillance were divided into cohorts A (chemoimmunotherapy) and B (chemotherapy), respectively, while cohort C (chemoimmunotherapy) comprised patients undergoing post-therapy PET/CT. Associations between SUVmax and major pathologic response (MPR) were evaluated through receiver operating characteristic (ROC) curves. RESULTS A total of 129 cases with an MPR rate of 46.5 % was identified. In neoadjuvant chemoimmunotherapy, ΔSUVmax% (AUC: 0.890, 95 % CI: 0.761-0.949) and post-therapy SUVmax (AUC: 0.933, 95 % CI: 0.802-0.959) could accurately predict MPR. On the contrary, the baseline SUVmax was not associated with MPR (p = 0.184). Furthermore, an independent cohort C proved that post-therapy SUVmax could serve as an independent predictor (AUC: 0.928, 95 % CI: 0.823-0.958). In addition, robust predictive performance could be observed when we use the optimal cut-off point of both ΔSUVmax% (54.4 %, AUC: 0.912, 95 % CI: 0.824-0.994) and post-therapy SUVmax (3.565, AUC: 0.912, 95 % CI: 0.824-0.994) in neoadjuvant chemoimmunotherapy. The RNA data revealed that the expression of PFKFB4, a key enzyme in glycolysis, was positively correlated with SUVmax value and tumor cell proliferation after neoadjuvant chemoimmunotherapy. CONCLUSION These findings highlighted that the ΔSUVmax% and remained SUVmax were accurate and non-invasive tests for the prediction of MPR after neoadjuvant chemoimmunotherapy.
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Affiliation(s)
- Fenghui Zhuang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - E Haoran
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Jia Huang
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Junqi Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Qiang Li
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Chongwu Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Yue Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Minglei Yang
- Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, People's Republic of China
| | - Minjie Ma
- Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, People's Republic of China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Hezhong Chen
- Department of Thoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai, People's Republic of China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China; Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, People's Republic of China; Linhai First People's Hospital, Taizhou, Zhejiang Province, China.
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21
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Gao PF, Lu N, Liu W. MRI VS. FDG-PET for diagnosis of response to neoadjuvant therapy in patients with locally advanced rectal cancer. Front Oncol 2023; 13:1031581. [PMID: 36741013 PMCID: PMC9890074 DOI: 10.3389/fonc.2023.1031581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/02/2023] [Indexed: 01/19/2023] Open
Abstract
Aim In this study, we aimed to compare the diagnostic values of MRI and FDG-PET for the prediction of the response to neoadjuvant chemoradiotherapy (NACT) of patients with locally advanced Rectal cancer (RC). Methods Electronic databases, including PubMed, Embase, and the Cochrane library, were systematically searched through December 2021 for studies that investigated the diagnostic value of MRI and FDG-PET in the prediction of the response of patients with locally advanced RC to NACT. The quality of the included studies was assessed using QUADAS. The pooled sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), and the area under the ROC (AUC) of MRI and FDG-PET were calculated using a bivariate generalized linear mixed model, random-effects model, and hierarchical regression. Results A total number of 74 studies with recruited 4,105 locally advanced RC patients were included in this analysis. The pooled sensitivity, specificity, PLR, NLR, and AUC for MRI were 0.83 (95% CI: 0.77-0.88), 0.85 (95% CI: 0.79-0.89), 5.50 (95% CI: 4.11-7.35), 0.20 (95% CI: 0.14-0.27), and 0.91 (95% CI: 0.88-0.93), respectively. The summary sensitivity, specificity, PLR, NLR and AUC for FDG-PET were 0.81 (95% CI: 0.77-0.85), 0.75 (95% CI: 0.70-0.80), 3.29 (95% CI: 2.64-4.10), 0.25 (95% CI: 0.20-0.31), and 0.85 (95% CI: 0.82-0.88), respectively. Moreover, there were no significant differences between MRI and FDG-PET in sensitivity (P = 0.565), and NLR (P = 0.268), while the specificity (P = 0.006), PLR (P = 0.006), and AUC (P = 0.003) of MRI was higher than FDG-PET. Conclusions MRI might superior than FGD-PET for the prediction of the response of patients with locally advanced RC to NACT.
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Affiliation(s)
- Peng Fei Gao
- Department of Traditional Chinese medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Na Lu
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China
| | - Wen Liu
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China,*Correspondence: Wen Liu,
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22
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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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23
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Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
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Luo Z, Li J, Liao Y, Huang W, Li Y, Shen X. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:611-626. [PMID: 37005907 DOI: 10.3233/xst-221352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
PURPOSE This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.
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Affiliation(s)
- Zhendong Luo
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jing Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Wenxiao Huang
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yulin Li
- Department of Radiology, Peking Universtiy Shenzhen Hospital, Shenzhen, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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25
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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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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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Spolverato G, Crimì F, Pucciarelli S. Imaging for guiding a more tailored approach in rectal cancer patients. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:811. [PMID: 36035009 PMCID: PMC9403946 DOI: 10.21037/atm-22-3498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/20/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Gaya Spolverato
- General Surgery 3, Department of Surgery, Oncology, and Gastroenterology, University of Padova, Padova, Italy
| | - Filippo Crimì
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, Padova, Italy
| | - Salvatore Pucciarelli
- General Surgery 3, Department of Surgery, Oncology, and Gastroenterology, University of Padova, Padova, Italy
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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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Capelli G, Campi C, Bao QR, Morra F, Lacognata C, Zucchetta P, Cecchin D, Pucciarelli S, Spolverato G, Crimì F. 18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy. Nucl Med Commun 2022; 43:815-822. [PMID: 35471653 PMCID: PMC9177153 DOI: 10.1097/mnm.0000000000001570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/05/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery. METHODS Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis. RESULTS Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively. CONCLUSION PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.
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Affiliation(s)
- Giulia Capelli
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | | | - Quoc Riccardo Bao
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Francesco Morra
- Institute of Radiology, Department of Medicine, University of Padova
| | | | - Pietro Zucchetta
- Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Salvatore Pucciarelli
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Gaya Spolverato
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Filippo Crimì
- Institute of Radiology, Department of Medicine, University of Padova
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30
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Quraishi MI. Radiomics-Guided Precision Medicine Approaches for Colorectal Cancer. Front Oncol 2022; 12:872656. [PMID: 35756680 PMCID: PMC9218262 DOI: 10.3389/fonc.2022.872656] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of precision oncology entails molecular profiling of tumors to guide therapeutic interventions. Genomic testing through next-generation sequencing (NGS) molecular analysis provides the basis of such highly targeted therapeutics in oncology. As radiomic analysis delivers an array of structural and functional imaging-based biomarkers that depict these molecular mechanisms and correlate with key genetic alterations related to cancers. There is an opportunity to synergize these two big-data approaches to determine the molecular guidance for precision therapeutics. Colorectal cancer is one such disease whose therapeutic management is being guided by genetic and genomic analyses. We review the rationale and utility of radiomics as a combinative strategy for these approaches in the management of colorectal cancer.
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Affiliation(s)
- Mohammed I Quraishi
- Department of Radiology, University of Tennessee Medical Center, Knoxville, TN, United States
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31
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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: 18] [Impact Index Per Article: 9.0] [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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32
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Yang C, Jiang Z, Cheng T, Zhou R, Wang G, Jing D, Bo L, Huang P, Wang J, Zhang D, Jiang J, Wang X, Lu H, Zhang Z, Li D. Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:893103. [PMID: 35600395 PMCID: PMC9121398 DOI: 10.3389/fonc.2022.893103] [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: 03/10/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation. Methods Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk. Results A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957–0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71–0.95) and 0.82 (95% CI: 0.68–0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88–0.93). Conclusion Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.
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Affiliation(s)
- Chao Yang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Tingting Cheng
- Department of General Practice, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Rongrong Zhou
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Guangcan Wang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Di Jing
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Jianbo Wang
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Daizhou Zhang
- Shandong Provincial Key Laboratory of Mucosal and Transdermal Drug Delivery Technologies, Shandong Academy of Pharmaceutical Sciences, Jinan, China
| | - Jianwei Jiang
- Optical and Digital Image Processing Division, Qingdao NovelBeam Technology Co., Ltd., Qingdao, China
| | - Xing Wang
- Software Research and Development Center, Shangdong AccurDx Diagnosis of Biotech Co., Ltd., Jinan, China
| | - Hua Lu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zijian Zhang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
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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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34
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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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Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062941] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.
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Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W. Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma. Front Oncol 2022; 12:802234. [PMID: 35273911 PMCID: PMC8901998 DOI: 10.3389/fonc.2022.802234] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/31/2022] [Indexed: 12/22/2022] Open
Abstract
Purpose To establish and verify a predictive model involving multiparameter MRI and clinical manifestations for predicting synchronous lung metastases (SLM) in osteosarcoma. Materials and Methods Seventy-eight consecutive patients with osteosarcoma (training dataset, n = 54; validation dataset, n = 24) were enrolled in our study. MRI features were extracted from the T1‐weighted image (T1WI), T2‐weighted image (T2WI), and contrast-enhanced T1-weighted image (CE-T1WI) of each patient. Least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression were performed to select key features and build radiomics models in conjunction with logistic regression (LR) and support vector machine (SVM) classifiers. Eight individual models based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, T1WI+T2WI+CE-T1WI, and clinical features, as well as two combined models, were built. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were employed to assess the different models. Results Tumor size was the most significant univariate clinical indicator (1). The AUC values of the LR predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.686, 0.85, 0.87, 0.879, 0.736, 0.85, and 0.914, respectively (2). The AUC values of the SVM predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI +CE-T1WI, T2WI +CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.629, 0.829, 0.771, 0.879, 0.643, 0.829, and 0.929, respectively (3). The AUC values of the clinical, combined 1 (clinical and LR-radiomics) and combined 2 (clinical and SVM-radiomics) predictive models were 0.779, 0.957, and 0.943, respectively. Conclusion The combined model exhibited good performance in predicting osteosarcoma SLM and may be helpful in clinical decision-making.
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Affiliation(s)
- Zhendong Luo
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Jing Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - YuTing Liao
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Shanghai, China
| | - RengYi Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Fan C, Sun K, Min X, Cai W, Lv W, Ma X, Li Y, Chen C, Zhao P, Qiao J, Lu J, Guo Y, Xia L. Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values. Eur J Radiol 2022; 148:110158. [DOI: 10.1016/j.ejrad.2022.110158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/04/2022] [Accepted: 01/11/2022] [Indexed: 11/03/2022]
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External Validation of a Radiomics Model for the Prediction of Complete Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer. Cancers (Basel) 2022; 14:cancers14041079. [PMID: 35205826 PMCID: PMC8870201 DOI: 10.3390/cancers14041079] [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: 12/27/2021] [Revised: 02/10/2022] [Accepted: 02/13/2022] [Indexed: 12/29/2022] Open
Abstract
Objective: Our objective was to develop a radiomics model based on magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CE-CT) to predict pathological complete response (pCR) to neoadjuvant treatment in locally advanced rectal cancer (LARC). Material: All patients treated for a LARC with neoadjuvant CRT and subsequent surgery in two separate institutions between 2012 and 2019 were considered. Both pre-CRT pelvic MRI and CE-CT were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial MRI sequences and on CE-CT. In total, 88 radiomic parameters were extracted from each sequence using the Miras© software, with a total of 822 features by patient. The cohort was split into training (Institution 1) and testing (Institution 2) sets. The ComBat and Synthetic Minority Over-sampling Technique (SMOTE) approaches were used to account for inter-institution heterogeneity and imbalanced data, respectively. We selected the most predictive characteristics using Spearman's rank correlation and the Area Under the ROC Curve (AUC). Five pCR prediction models (clinical, radiomics before and after ComBat, and combined before and after ComBat) were then developed on the training set with a neural network approach and a bootstrap internal validation (n = 1000 replications). A cut-off maximizing the model's performance was defined on the training set. Each model was then evaluated on the testing set using sensitivity, specificity, balanced accuracy (Bacc) with the predefined cut-off. Results: Out of the 124 included patients, 14 had pCR (11.3%). After ComBat harmonization, the radiomic and the combined models obtained a Bacc of 68.2% and 85.5%, respectively, while the clinical model and the pre-ComBat combined achieved respective Baccs of 60.0% and 75.5%. Conclusions: After correction of inter-site variability and imbalanced data, addition of radiomic features enhances the prediction of pCR after neoadjuvant CRT in LARC.
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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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Lv L, Xin B, Hao Y, Yang Z, Xu J, Wang L, Wang X, Song S, Guo X. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT. J Transl Med 2022; 20:66. [PMID: 35109864 PMCID: PMC8812058 DOI: 10.1186/s12967-022-03262-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background To develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer. Methods A total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed. Results Radiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax. Conclusion This study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer. Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03262-5.
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Affiliation(s)
- Lilang Lv
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yichao Hao
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Ziyi Yang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Junyan Xu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Lisheng Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. .,Center for Biomedical Imaging, Fudan University, Shanghai, China. .,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.
| | - Xiaomao Guo
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 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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Dual time point imaging of staging PSMA PET/CT quantification; spread and radiomic analyses. Ann Nucl Med 2022; 36:310-318. [DOI: 10.1007/s12149-021-01705-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/29/2021] [Indexed: 11/01/2022]
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Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm. Eur J Radiol 2022; 147:110146. [PMID: 34998098 DOI: 10.1016/j.ejrad.2021.110146] [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] [Received: 12/07/2021] [Revised: 12/28/2021] [Accepted: 12/30/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVE The aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC). METHODS Two populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification. Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance. The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated. RESULTS Four features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833). Both inter- and intra-reader evaluation showed good agreement (κ > 0.6). CONCLUSIONS The proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.
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van Zoggel DMGI, Voogt ELK, van Lijnschoten IG, Cnossen JS, Creemers GJ, Nederend J, Bloemen JG, Nieuwenhuijzen GAP, Burger PJWA, Lardenoije SGGF, Rutten HJT, Roef MJ. Metabolic positron emission tomography/CT response after induction chemotherapy and chemo(re)irradiation is associated with higher negative resection margin rate in patients with locally recurrent rectal cancer. Colorectal Dis 2022; 24:59-67. [PMID: 34601782 DOI: 10.1111/codi.15934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/19/2021] [Accepted: 09/27/2021] [Indexed: 02/08/2023]
Abstract
AIM Positron emission tomography (PET)/CT can be used to monitor the metabolic changes that occur after intensified treatment with induction chemotherapy and chemo(re)irradiation for locally recurrent rectal cancer (LRRC). This study aimed to analyse the correlation between the PET/CT response and final histopathological outcomes. METHODS All LRRC patients who underwent induction chemotherapy prior to surgery between January 2010 and July 2020 and were monitored with pretreatment and post-treatment PET/CT were included. Visual qualitative analysis was performed, and patients were scored as having achieved a complete metabolic response (CMR), partial metabolic response (PMR) or no response (NR). The histopathological response was assessed according to the Mandard tumour regression (TRG) score and categorized as major (TRG 1-2), partial (TRG 3) or poor (TRG 4-5). The PET/CT and TRG categories were compared, and possible confounders were analysed. RESULTS A total of 106 patients were eligible for analysis; 24 (23%) had a CMR, 54 (51%) had a PMR and 28 (26%) had NR. PET/CT response was a significant predictor of the negative resection margin rate, achieving 96% for CMR, 69% for PMR and 50% for NR. The overall accuracy between PET score and pathological TRG was 45%, and the positive predictive value for CMR was 63%. A longer interval between post-treatment PET/CT and surgery negatively influenced the predictive value. CONCLUSION Metabolic PET/CT response evaluation after neoadjuvant treatment proves to be a complementary diagnostic tool to standard MRI in assessing tumour response, and may play a role for treatment planning in LRRC patients.
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Affiliation(s)
| | - Eva L K Voogt
- Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | | | - Jeltsje S Cnossen
- Department of Radiotherapy, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Geert-Jan Creemers
- Department of Medical Oncology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Johanne G Bloemen
- Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | | | - Pim J W A Burger
- Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | | | - Harm J T Rutten
- Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Mark J Roef
- Department of Nuclear Medicine, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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46
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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: 15] [Impact Index Per Article: 5.0] [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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Low-Rank Matrix Denoising Algorithm-Based MRI Image Feature for Therapeutic Effect Evaluation of NCRT on Rectal Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3080640. [PMID: 34880974 PMCID: PMC8648445 DOI: 10.1155/2021/3080640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/31/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022]
Abstract
This study aimed to explore the therapeutic effects of neoadjuvant chemoradiotherapy (NCRT) on rectal cancer patients using the MRI based on low-rank matrix denoising algorithm, which was then compared with the postoperative pathological examination to evaluate its application value in tumor staging after NCRT treatment. 15 patients with rectal cancer who met the requirements of radiotherapy and chemotherapy after conventional MRI were selected as the research subjects. The conventional MRI images before and after NCRT treatment were divided in two groups. One group was not processed and set as the conventional group; the other group was processed with low-rank matrix denoising algorithm and set as the optimized group. The two groups of images were observed for the changes in the ADC value and length and thickness of the tumor before and after NCRT treatment. The two groups were compared with the pathological examination for the complete remission of pathology (pCR) after the NCRT treatment and the tumor stage results. The results showed that Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) (18.9121 and 74.9911 dB) after introducing the low-rank matrix denoising algorithm were significantly better than those before (20.1234 and 70.1234 dB) (P < 0.05); there were notable differences in the tumor index data within the two groups before and after NCRT treatment (P < 0.05), indicating that the NCRT treatment was effective. The pathological examination results of pCR data of the two groups were not much different (P > 0.05); the examination results between the two groups were different, but no notable difference was noted (P < 0.05); in the optimized group, there was no notable difference between the MRI results and the pathological examination results (P < 0.05), while in the conventional group, there were notable differences in the MRI results and pathological examination results (P < 0.05). In conclusion, MRI images based on low-rank matrix denoising algorithm are clearer, which can improve the diagnosis rate of patients and better display the changes of the microenvironment after NCRT treatment. It also indicates that NCRT treatment has significant clinical effects in the treatment of rectal cancer patients, which is worth promoting.
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Zhang Z, He K, Wang Z, Zhang Y, Wu D, Zeng L, Zeng J, Ye Y, Gu T, Xiao X. Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study. Front Oncol 2021; 11:779202. [PMID: 34869030 PMCID: PMC8636428 DOI: 10.3389/fonc.2021.779202] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 10/29/2021] [Indexed: 12/28/2022] Open
Abstract
Purpose To evaluate whether multiparametric magnetic resonance imaging (MRI)-based logistic regression models can facilitate the early prediction of chemoradiotherapy response in patients with residual brain gliomas after surgery. Patients and Methods A total of 84 patients with residual gliomas after surgery from January 2015 to September 2020 who were treated with chemoradiotherapy were retrospectively enrolled and classified as treatment-sensitive or treatment-insensitive. These patients were divided into a training group (from institution 1, 57 patients) and a validation group (from institutions 2 and 3, 27 patients). All preoperative and postoperative MR images were obtained, including T1-weighted (T1-w), T2-weighted (T2-w), and contrast-enhanced T1-weighted (CET1-w) images. A total of 851 radiomics features were extracted from every imaging series. Feature selection was performed with univariate analysis or in combination with multivariate analysis. Then, four multivariable logistic regression models derived from T1-w, T2-w, CET1-w and Joint series (T1+T2+CET1-w) were constructed to predict the response of postoperative residual gliomas to chemoradiotherapy (sensitive or insensitive). These models were validated in the validation group. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to compare the predictive performances of these models. Results Four models were created and showed the following areas under the ROC curves (AUCs) in the training and validation groups: Model-Joint series (AUC, 0.923 and 0.852), Model-T1 (AUC, 0.835 and 0.809), Model-T2 (AUC, 0.784 and 0.605), and Model-CET1 (AUC, 0.805 and 0.537). These results indicated that the Model-Joint series had the best performance in the validation group, followed by Model-T1, Model-T2 and finally Model-CET1. The calibration curves indicated good agreement between the Model-Joint series predictions and actual probabilities. Additionally, the DCA curves demonstrated that the Model-Joint series was clinically useful. Conclusion Multiparametric MRI-based radiomics models can potentially predict tumor response after chemoradiotherapy in patients with postoperative residual gliomas, which may aid clinical decision making, especially to help patients initially predicted to be treatment-insensitive avoid the toxicity of chemoradiotherapy.
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Affiliation(s)
- Zhaotao Zhang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Keng He
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhenhua Wang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Youming Zhang
- Department of Radiology, Hsiang-ya Hospital, Changsha, China
| | - Di Wu
- Department of Radiology, The First Affiliated Hospital of Gannan Medical College, Ganzhou, China
| | - Lei Zeng
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junjie Zeng
- Department of Radiology, The Fifth Affiliated Hospital of Jinan University, Heyuan, China
| | - Yinquan Ye
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Taifu Gu
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xinlan Xiao
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Cheng Y, Luo Y, Hu Y, Zhang Z, Wang X, Yu Q, Liu G, Cui E, Yu T, Jiang X. Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol (NY) 2021; 46:5072-5085. [PMID: 34302510 DOI: 10.1007/s00261-021-03219-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the value of multiparametric MRI-based radiomics on predicting response to nCRT in patients with rectal cancer. METHODS This study enrolled 193 patients with pathologically confirmed LARC who received nCRT treatment between Apr. 2014 and Jun. 2018. All patients underwent baseline T1-weighted (T1W), T2-weighted (T2W) and T2-weighted fat-suppression (T2FS) MRI scans before neoadjuvant chemoradiotherapy. Radiomics features were extracted and selected from the MRI data to establish the radiomics signature. Important clinical predictors were identified by Mann-Whitney U test and Chi-square test. The nomogram integrating the radiomics signature and important clinical predictors was constructed using multivariate logistic regression. Prediction capabilities of each model were assessed with receiver operating characteristic (ROC) curve analysis. Performance of the nomogram was evaluated by its calibration and potential clinical usefulness. RESULTS For the prediction of good response (GR) and pathologic complete response (pCR), the developed radiomics signature comprising 10 and 7 features, respectively, were significantly associated with the therapeutic response to nCRT. The nomogram incorporating the radiomics signature and important clinical predictors (CEA and CA19-9 for predicting GR; CEA, posttreatment length and posttreatment thickness for predicting pCR) achieved favorable prediction efficacy, with AUCs of 0.918 (95% confidence interval [CI]: 0.867-0.971, Sen = 0.972, Spe = 0.828) and 0.944 (95% CI: 0.891-0.997, Sen = 0.943, Spe = 0.828) in the training and validation cohort for predicting GR, respectively; with AUCs of 0.959 (95% CI: 0.927-0.991, Sen = 1.000, Spe = 0.833) and 0.912 (95% CI: 0.843-0.982, Sen = 1.000, Spe = 0.815) in the training and validation cohort for predicting pCR, respectively. Decision curve analysis confirmed potential clinical usefulness of our nomogram. CONCLUSIONS This study demonstrated that the MRI-based radiomics nomogram is predictive of response to nCRT and can be considered as a promising tool for facilitating treatment decision-making for patients with LARC.
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Affiliation(s)
- Yuan Cheng
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yue Hu
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, People's Republic of China
| | - Zhaohe Zhang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xingling Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Qing Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Guanyu Liu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Enuo Cui
- School of Computer Science and Engineering, Shenyang University, Shenyang, 110044, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, People's Republic of China.
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Lee SW, Jeong SY, Kim K, Kim SJ. Direct comparison of F-18 FDG PET/CT and MRI to predict pathologic response to neoadjuvant treatment in locally advanced rectal cancer: a meta-analysis. Ann Nucl Med 2021; 35:1038-1047. [PMID: 34109555 DOI: 10.1007/s12149-021-01639-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/06/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE The purpose of the current study was to compare the diagnostic accuracies of F-18 FDG PET/CT and MRI for prediction of pathologic responses to neoadjuvant treatment (NAT) in locally advanced rectal cancer (LARC) patients based on a systematic review and meta-analyses. METHODS The PubMed, Cochrane, and Embase databases were searched to identify studies that conducted direct comparisons of the diagnostic performance of F-18 FDG PET/CT and MRI for the prediction of pathologic response to NAT in patients with LARC from the earliest available date of indexing up to July 31, 2020. We determined the sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LR + and LR -), and we constructed summary receiver operating characteristic curves. RESULTS In nine studies (427 patients), the pooled sensitivity of F-18 FDG PET/CT was 0.79 (95% CI 0.71-0.86) and the pooled specificity was 0.74 (95% CI 0.60-0.84). LR syntheses yielded an overall LR + of 3.1 (95% CI 1.9-5.0) and an LR - of 0.28 (95% CI 0.18-0.43). The pooled diagnostic odds ratio (DOR) was 11 (95% CI 5-26). The pooled sensitivity of MRI was 0.89 (95% CI 0.77-0.95) and the pooled specificity was 0.66 (95% CI 0.55-0.76). LR syntheses yielded an overall LR + of 2.6 (95% CI 1.9-3.6) and an LR - of 0.17 (95% CI 0.08-0.37). The pooled DOR was 15 (95% CI 6-42). In meta-regression analysis, no variable was identified as the source of the study heterogeneity. CONCLUSION F-18 FDG PET/CT and MRI showed similar diagnostic performances for the prediction of pathologic responses to NAT in patients with LARC. However, each modality can be a complement to other rather than being used singly.
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Affiliation(s)
- Sang-Woo Lee
- Department of Nuclear Medicine, Kyungpook National University Chilgok Hospital and School of Medicine, Daegu, 41404, Republic of Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, Kyungpook National University Chilgok Hospital and School of Medicine, Daegu, 41404, Republic of Korea
| | - Keunyoung Kim
- Department of Nuclear Medicine, Pusan National University Hospital, Busan, 49241, Republic of Korea
| | - Seong-Jang Kim
- Department of Nuclear Medicine, College of Medicine, Pusan National University Yangsan Hospital, Yangsan, 50612, Republic of Korea.
- BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, 50612, Republic of Korea.
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