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Löhr JM, Öhlund D, Söreskog E, Andersson E, Vujasinovic M, Zethraeus N, Sund M. Can our experience with surveillance for inherited pancreatic cancer help to identify early pancreatic cancer in the general population? Fam Cancer 2024; 23:399-403. [PMID: 38441833 PMCID: PMC11255073 DOI: 10.1007/s10689-024-00363-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 02/05/2024] [Indexed: 07/18/2024]
Abstract
Screening of the general population for cancer is a matter of primary prevention reducing the burden of disease. Whilst this is successful for several cancers including breast, colon and prostate, the situation to screen and hence prevent pancreatic cancer is different. The organ is not as accessible to simple physical exam or biological samples (fecal or blood test). Neither exists a blood test such as PSA that is cost-effective. Reviewing the evidence from screening risk groups for pancreatic cancer, one must conclude that there is no rational at present to screen the general population, for a lack of appropriate tests.
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Affiliation(s)
- J-Matthias Löhr
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden.
- Div. of Surgery & Oncology, Dept. of Upper Abdominal Diseases, CLINTEC Karolinska Institutet, Karolinska Comprehensive Cancer Center, Stockholm, SE-141 86, Sweden.
| | - Daniel Öhlund
- Department of Radiation Sciences and Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden
| | - Emma Söreskog
- Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Stockholm, Sweden
| | - Emil Andersson
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden
| | - Miroslav Vujasinovic
- Karolinska Comprehensive Cancer Center and Karolinska Institutet, Stockholm, Sweden
| | - Niklas Zethraeus
- Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Stockholm, Sweden
| | - Malin Sund
- Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Surgical and Perioperative Sciences/ Surgery, Umeå University, Umeå, Sweden
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Zheng J, Liu W, Chen J, Sun Y, Chen C, Li J, Yi C, Zeng G, Chen Y, Song W. Differential diagnostic value of radiomics models in benign versus malignant vertebral compression fractures: A systematic review and meta-analysis. Eur J Radiol 2024; 178:111621. [PMID: 39018646 DOI: 10.1016/j.ejrad.2024.111621] [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: 12/08/2023] [Revised: 06/29/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024]
Abstract
PURPOSE Early diagnosis of benign and malignant vertebral compression fractures by analyzing imaging data is crucial to guide treatment and assess prognosis, and the development of radiomics made it an alternative option to biopsy examination. This systematic review and meta-analysis was conducted with the purpose of quantifying the diagnostic efficacy of radiomics models in distinguishing between benign and malignant vertebral compression fractures. METHODS Searching on PubMed, Embase, Web of Science and Cochrane Library was conducted to identify eligible studies published before September 23, 2023. After evaluating for methodological quality and risk of bias using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), we selected studies providing confusion matrix results to be included in random-effects meta-analysis. RESULTS A total of sixteen articles, involving 1,519 vertebrae with pathological-diagnosed tumor infiltration, were included in our meta-analysis. The combined sensitivity and specificity of the top-performing models were 0.92 (95 % CI: 0.87-0.96) and 0.93 (95 % CI: 0.88-0.96), respectively. Their AUC was 0.97 (95 % CI: 0.96-0.99). By contrast, radiologists' combined sensitivity was 0.90 (95 %CI: 0.75-0.97) and specificity was 0.92 (95 %CI: 0.67-0.98). The AUC was 0.96 (95 %CI: 0.94-0.97). Subsequent subgroup analysis and sensitivity test suggested that part of the heterogeneity might be explained by differences in imaging modality, segmentation, deep learning and cross-validation. CONCLUSION We found remarkable diagnosis potential in correctly distinguishing vertebral compression fractures in complex clinical contexts. However, the published radiomics models still have a great heterogeneity, and more large-scale clinical trials are essential to validate their generalizability.
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Affiliation(s)
- Jiayuan Zheng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Wenzhou Liu
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jianan Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yujun Sun
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chen Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jiajie Li
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chunyan Yi
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Gang Zeng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yanbo Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Weidong Song
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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Horvat N, Papanikolaou N, Koh DM. Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use. Radiol Artif Intell 2024; 6:e230437. [PMID: 38717290 DOI: 10.1148/ryai.230437] [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] [Indexed: 05/12/2024]
Abstract
Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.
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Affiliation(s)
- Natally Horvat
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Nikolaos Papanikolaou
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Dow-Mu Koh
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
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Adili D, Mohetaer A, Zhang W. Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis. Eur J Radiol 2024; 173:111249. [PMID: 38382422 DOI: 10.1016/j.ejrad.2023.111249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, a lingering debate persists regarding the accuracy of these predictions. Against this backdrop, this study was conducted to examine the accuracy of radiomics in predicting the response to neoadjuvant chemotherapy in GC patients. METHODS An exhaustive search of relevant studies was conducted in PubMed, Cochrane, Embase, and Web of Science databases up to February 21, 2023. The radiomics quality scoring (RQS) tool was employed to assess study quality. Tumor response to neoadjuvant chemotherapy and survival outcomes were examined as outcome measures. RESULTS Fourteen studies involving 3,373 GC patients who had received neoadjuvant chemotherapy were incorporated in our meta-analysis. The mean RQS score across all studies was 36.3%, ranging between 0 and 63.9%. On the validation cohort, when the modeling variables were restricted to radiomic features alone, the predictive performance was characterized by a c-index of 0.750 (95% CI: 0.710-0.790), with a sensitivity of 0.67 (95% CI: 0.58-0.75) and a specificity of 0.77 (95% CI: 0.69-0.84) for the prediction of neoadjuvant chemotherapy response. When clinical data was integrated with radiomic features as modeling variables, the predictive performance improved, yielding a c-index of 0.814 (95% CI: 0.780-0.847), a sensitivity of 0.78 [95% CI: 0.70-0.84], and a specificity of 0.73 [95% CI: 0.67-0.79]. CONCLUSIONS Radiomics holds promise to noninvasively predict neoadjuvant chemotherapy response and survival outcomes among patients with locally advanced GC. Additionally, we underscore the need for future multicenter studies and the development of imaging-sourced tools for risk stratification in this patient population.
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Affiliation(s)
- Diliyaer Adili
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
| | - Aibibai Mohetaer
- Department of Cardiology, The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063 China
| | - Wenbin Zhang
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
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Huang ML, Ren J, Jin ZY, Liu XY, Li Y, He YL, Xue HD. Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:439-456. [PMID: 38349417 DOI: 10.1007/s11547-024-01765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/03/2024] [Indexed: 03/16/2024]
Abstract
PURPOSE We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Lee JH, Shin J, Min JH, Jeong WK, Kim H, Choi SY, Lee J, Hong S, Kim K. Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features. Cancer Imaging 2024; 24:6. [PMID: 38191489 PMCID: PMC10775464 DOI: 10.1186/s40644-024-00653-3] [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: 07/26/2023] [Accepted: 12/29/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer. METHODS We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity. RESULTS Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort. CONCLUSIONS The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.
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Affiliation(s)
- Jeong Hyun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jaeseung Shin
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Ji Hye Min
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Honsoul Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Seo-Youn Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea
| | - Jisun Lee
- Department of Radiology, College of Medicine, Chungbuk National University, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Sungjun Hong
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
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Del Chiaro M, Sugawara T, Karam SD, Messersmith WA. Advances in the management of pancreatic cancer. BMJ 2023; 383:e073995. [PMID: 38164628 DOI: 10.1136/bmj-2022-073995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Pancreatic cancer remains among the malignancies with the worst outcomes. Survival has been improving, but at a slower rate than other cancers. Multimodal treatment, including chemotherapy, surgical resection, and radiotherapy, has been under investigation for many years. Because of the anatomical characteristics of the pancreas, more emphasis on treatment selection has been placed on local extension into major vessels. Recently, the development of more effective treatment regimens has opened up new treatment strategies, but urgent research questions have also become apparent. This review outlines the current management of pancreatic cancer, and the recent advances in its treatment. The review discusses future treatment pathways aimed at integrating novel findings of translational and clinical research.
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Affiliation(s)
- Marco Del Chiaro
- Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
- University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
| | - Toshitaka Sugawara
- Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Sana D Karam
- University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Wells A Messersmith
- University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA
- Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
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Qu W, Yang J, Li J, Yuan G, Li S, Chu Q, Xie Q, Zhang Q, Cheng B, Li Z. Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy. Br J Radiol 2023; 96:20221112. [PMID: 37195026 PMCID: PMC10607397 DOI: 10.1259/bjr.20221112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 05/11/2023] [Indexed: 05/18/2023] Open
Abstract
OBJECTIVE This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA). METHODS 498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients. RESULTS The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution. CONCLUSION The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA. ADVANCES IN KNOWLEDGE This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.
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Affiliation(s)
- Weinuo Qu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiannan Yang
- School of Data Science, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qingguo Xie
- Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | | | - Bin Cheng
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Wang C, Chen J, Zheng N, Zheng K, Zhou L, Zhang Q, Zhang W. Predicting the risk of distant metastasis in patients with locally advanced rectal cancer using model based on pre-treatment T2WI-based radiomic features plus postoperative pathological stage. Front Oncol 2023; 13:1109588. [PMID: 37746305 PMCID: PMC10517628 DOI: 10.3389/fonc.2023.1109588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To assess the prognostic value of a model based on pre-treatment T2WI-based radiomic features and postoperative pathological staging in patients with locally advanced rectal cancer who have undergone neoadjuvant chemoradiotherapy. Methods Radiomic features were derived from T2WI, and a radiomic signature (RS) was established and validated for the prediction of distant metastases (DM). Subsequently, we designed and validated a nomogram model that combined the radiomic signature and postoperative pathological staging for enhanced DM prediction. Performance measures such as the concordance index (C-index) and area under the curve (AUC) were computed to assess the predictive accuracy of the models. Results A total of 260 patients participated in this study, of whom 197 (75.8%) were male, and the mean age was 57.2 years with a standard deviation of 11.2 years. 15 radiomic features were selected to define the radiomic signature. Patients with a high-risk radiomic signature demonstrated significantly shorter distant metastasis-free survival (DMFS) in both the development and validation cohorts. A nomogram, incorporating the radiomic signature, pathological T stage, and N stage, achieved an area under the curve (AUC) value of 0.72 (95% CI, 0.60-0.83) in the development cohort and 0.83 (95% CI, 0.73-0.92) in the validation cohort. Conclusion A radiomic signature derived from T2WI-based radiomic features can effectively distinguish patients with varying risks of DM. Furthermore, a nomogram integrating the radiomic signature and postoperative pathological stage proves to be a robust predictor of DMFS.
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Affiliation(s)
- Chen Wang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Jingjing Chen
- Graduate School of Naval Medical University, Shanghai, China
| | - Nanxin Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Kuo Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Lu Zhou
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
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10
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Jiang T, Zhao Z, Liu X, Shen C, Mu M, Cai Z, Zhang B. Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study. Front Oncol 2023; 13:1161237. [PMID: 37731636 PMCID: PMC10507631 DOI: 10.3389/fonc.2023.1161237] [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: 02/08/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
Background Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. Methods The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. Results Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. Conclusion The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. Study registration This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).
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Affiliation(s)
- Tianxiang Jiang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhou Zhao
- Department of Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Xueting Liu
- Department of Medical Discipline Construction, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyong Shen
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Mingchun Mu
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaolun Cai
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Zhang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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11
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Zhang Y, Yue X, Zhang P, Zhang Y, Wu L, Diao N, Ma G, Lu Y, Ma L, Tao K, Li Q, Han P. Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study. Front Oncol 2023; 13:1193010. [PMID: 37645430 PMCID: PMC10461453 DOI: 10.3389/fonc.2023.1193010] [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: 03/28/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023] Open
Abstract
Objective gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. Methods Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. Results The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621-0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. Conclusion The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaofei Yue
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuying Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Nan Diao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Guina Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuting Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd., Shanghai, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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12
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Huang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging 2023; 14:117. [PMID: 37395888 DOI: 10.1186/s13244-023-01464-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/11/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. METHODS Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. RESULTS Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range - 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50-49.13) and 12.55 (95% CI 8.38-18.77), respectively. CONCLUSION Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. CRITICAL RELEVANCE STATEMENT Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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13
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [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: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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14
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Volpe S, Gaeta A, Colombo F, Zaffaroni M, Mastroleo F, Vincini MG, Pepa M, Isaksson LJ, Turturici I, Marvaso G, Ferrari A, Cammarata G, Santamaria R, Franzetti J, Raimondi S, Botta F, Ansarin M, Gandini S, Cremonesi M, Orecchia R, Alterio D, Jereczek-Fossa BA. Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit. Cancers (Basel) 2023; 15:cancers15072022. [PMID: 37046683 PMCID: PMC10093133 DOI: 10.3390/cancers15072022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/23/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models’ performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients.
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15
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Hinzpeter R, Kulanthaivelu R, Kohan A, Avery L, Pham NA, Ortega C, Metser U, Haider M, Veit-Haibach P. CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling. Cancers (Basel) 2022; 14:cancers14246224. [PMID: 36551709 PMCID: PMC9776865 DOI: 10.3390/cancers14246224] [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: 10/26/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42-86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution's web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.
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Affiliation(s)
- Ricarda Hinzpeter
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Correspondence: ; Tel.: +1-416-340-4800
| | - Roshini Kulanthaivelu
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Andres Kohan
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Lisa Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Nhu-An Pham
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Masoom Haider
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
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16
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Yang J, Liu Y, Liu S. Comment on “Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer”. World J Gastroenterol 2022; 28:6310-6313. [PMID: 36504551 PMCID: PMC9730437 DOI: 10.3748/wjg.v28.i44.6310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/26/2022] [Accepted: 11/16/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies because of its high invasiveness and metastatic potential. Computed tomography (CT) is often used as a preliminary diagnostic tool for pancreatic cancer, and it is increasingly used to predict treatment response and disease stage. Recently, a study published in World Journal of Gastroenterology reported that quantitative analysis of preoperative enhanced CT data can be used to predict postoperative overall survival in patients with PDAC. A tumor relative enhancement ratio of ≤ 0.7 indicates a higher tumor stage and poor prognosis.
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Affiliation(s)
- Jian Yang
- Central Laboratory, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
| | - Ying Liu
- Department of Medical Oncology, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
| | - Shi Liu
- Central Laboratory, The Third Affiliated Hospital, Qiqihar Medical University, Qiqihar 161000, Heilongjiang Province, China
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17
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Barat M, Marchese U, Pellat A, Dohan A, Coriat R, Hoeffel C, Fishman EK, Cassinotto C, Chu L, Soyer P. Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances. Can Assoc Radiol J 2022; 74:351-361. [PMID: 36065572 DOI: 10.1177/08465371221124927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Ugo Marchese
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Digestive, Hepatobiliary and Pancreatic Surgery, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | | | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Christophe Cassinotto
- Department of Radiology, CHU Montpellier, 27037University of Montpellier, Saint-Éloi Hospital, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
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