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Castellana R, Fanni SC, Roncella C, Romei C, Natrella M, Neri E. Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Eur J Radiol 2024; 176:111510. [PMID: 38781919 DOI: 10.1016/j.ejrad.2024.111510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated. RESULTS Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively). CONCLUSION Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies.
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Affiliation(s)
- Roberto Castellana
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy.
| | - Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
| | - Claudia Roncella
- Radiology Unit, Apuane Hospital, Azienda USL Toscana Nord Ovest, Via Mattei 21, 54100, Massa, Italy
| | - Chiara Romei
- Department of Diagnostic Imaging, Diagnostic Radiology 2, Pisa University Hospital, Via Paradisa 2, 56124, Pisa, Italy
| | - Massimiliano Natrella
- Diagnostic and Interventional Radiology, "Parini" Regional Hospital, Azienda USL della Valle d'Aosta, Viale Ginevra 3 11100, Aosta, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
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Collarino A, Feudo V, Pasciuto T, Florit A, Pfaehler E, de Summa M, Bizzarri N, Annunziata S, Zannoni GF, de Geus-Oei LF, Ferrandina G, Gambacorta MA, Scambia G, Boellaard R, Sala E, Rufini V, van Velden FH. Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer? J Nucl Med 2024; 65:962-970. [PMID: 38548352 DOI: 10.2967/jnumed.123.267044] [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: 11/10/2023] [Revised: 03/15/2024] [Indexed: 06/05/2024] Open
Abstract
This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.
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Affiliation(s)
- Angela Collarino
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vanessa Feudo
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Tina Pasciuto
- Research Core Facility Data Collection G-STeP, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Anita Florit
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Elisabeth Pfaehler
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Marco de Summa
- PET/CT Center, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Nicolò Bizzarri
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Salvatore Annunziata
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Gian Franco Zannoni
- Gynecopathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Pathology, Department of Woman and Child Health and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands
- Department of Radiation Science and Technology, Technical University of Delft, Delft, The Netherlands
| | - Gabriella Ferrandina
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Maria Antonietta Gambacorta
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Roma, Italy
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands; and
| | - Evis Sala
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
- Advanced Radiodiagnostics Centre, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vittoria Rufini
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy;
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Floris Hp van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Deng L, Shuai P, Liu Y, Yong T, Liu Y, Li H, Zheng X. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis. Osteoporos Int 2024:10.1007/s00198-024-07136-y. [PMID: 38802557 DOI: 10.1007/s00198-024-07136-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
This study aimed to assess the diagnostic accuracy of radiomics for predicting osteoporosis and the quality of radiomic studies. The study protocol was prospectively registered on PROSPERO (CRD42023425058). We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from inception to June 1, 2023, for eligible articles that applied radiomic techniques to diagnosing osteoporosis or abnormal bone mass. Quality and risk of bias of the included studies were evaluated with radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tools. The data analysis utilized the R program with mada, metafor, and meta packages. Ten retrospective studies with 5926 participants were included in the systematic review and meta-analysis. The overall risk of bias and applicability concerns for each domain of the studies were rated as low, except for one study which was considered to have a high risk of flow and time bias. The mean METRICS score was 70.1% (range 49.6-83.2%). There was moderate heterogeneity across studies and meta-regression identified sources of heterogeneity in the data, including imaging modality, feature selection method, and classifier. The pooled diagnostic odds ratio (DOR) under the bivariate random effects model across the studies was 57.22 (95% CI 27.62-118.52). The pooled sensitivity and specificity were 87% (95% CI 81-92%) and 87% (95% CI 77-93%), respectively. The area under the summary receiver operating characteristic curve (AUC) of the radiomic models was 0.94 (range 0.8 to 0.98). The results supported that the radiomic techniques had good accuracy in diagnosing osteoporosis or abnormal bone mass. The application of radiomics in osteoporosis diagnosis needs to be further confirmed by more prospective studies with rigorous adherence to existing guidelines and multicenter validation.
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Affiliation(s)
- Ling Deng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Shuai
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Youren Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Yong
- Department of Medical Information Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuping Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaoxia Zheng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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Fiz F, Rossi N, Langella S, Conci S, Serenari M, Ardito F, Cucchetti A, Gallo T, Zamboni GA, Mosconi C, Boldrini L, Mirarchi M, Cirillo S, Ruzzenente A, Pecorella I, Russolillo N, Borzi M, Vara G, Mele C, Ercolani G, Giuliante F, Cescon M, Guglielmi A, Ferrero A, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model. Ann Surg Oncol 2024:10.1245/s10434-024-15457-9. [PMID: 38797789 DOI: 10.1245/s10434-024-15457-9] [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: 02/07/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices. METHODS All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study. The arterial and portal phases of CT performed fewer than 60 days before surgery were analyzed. A manual segmentation of the tumor was performed (Tumor-VOI). A 5-mm volume expansion then was applied to identify the peritumoral tissue (Margin-VOI). RESULTS The study enrolled 215 patients. After a median follow-up period of 28 months, the overall survival (OS) rate was 57.0%, and the progression-free survival (PFS) rate was 34.9% at 3 years. The clinical predictive model of OS had a C-index of 0.681. The addition of radiomic features led to a progressive improvement of performances (C-index of 0.71, including the portal Tumor-VOI, C-index of 0.752 including the portal Tumor- and Margin-VOI, C-index of 0.764, including all VOIs of the portal and arterial phases). The latter model combined clinical variables (CA19-9 and tumor pattern), tumor indices (density, homogeneity), margin data (kurtosis, compacity, shape), and GLRLM indices. The model had performance equivalent to that of the postoperative clinical model including the pathology data (C-index of 0.765). The same results were observed for PFS. CONCLUSIONS The radiomics of ICC and peritumoral tissue extracted from preoperative CT improves the prediction of survival. Both the portal and arterial phases should be considered. Radiomic and clinical data are complementary and achieve a preoperative estimation of prognosis equivalent to that achieved in the postoperative setting.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Ente Ospedaliero "Ospedali Galliera", Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, Tübingen, Germany
| | - Noemi Rossi
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Serena Langella
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Simone Conci
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Matteo Serenari
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Alessandro Cucchetti
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Teresa Gallo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Giulia A Zamboni
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Luca Boldrini
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | | | - Stefano Cirillo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Andrea Ruzzenente
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Nadia Russolillo
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Borzi
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Caterina Mele
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giorgio Ercolani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Matteo Cescon
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Alfredo Guglielmi
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Alessandro Ferrero
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General and Oncologic Surgery, Humanitas Gavazzeni University Hospital, Bergamo, Italy.
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Hinzpeter R, Mirshahvalad SA, Murad V, Avery L, Kulanthaivelu R, Kohan A, Ortega C, Elimova E, Yeung J, Hope A, Metser U, Veit-Haibach P. The [ 18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study. Cancers (Basel) 2024; 16:1873. [PMID: 38791955 PMCID: PMC11119256 DOI: 10.3390/cancers16101873] [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: 04/17/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
We aimed to investigate whether [18F]F-FDG-PET/CT-derived radiomics can classify histologic subtypes and determine the anatomical origin of various malignancies. In this IRB-approved retrospective study, 391 patients (age = 66.7 ± 11.2) with pulmonary (n = 142), gastroesophageal (n = 128) and head and neck (n = 121) malignancies were included. Image segmentation and feature extraction were performed semi-automatically. Two models (all possible subset regression [APS] and recursive partitioning) were employed to predict histology (squamous cell carcinoma [SCC; n = 219] vs. adenocarcinoma [AC; n = 172]), the anatomical origin, and histology plus anatomical origin. The recursive partitioning algorithm outperformed APS to determine histology (sensitivity 0.90 vs. 0.73; specificity 0.77 vs. 0.65). The recursive partitioning algorithm also revealed good predictive ability regarding anatomical origin. Particularly, pulmonary malignancies were identified with high accuracy (sensitivity 0.93; specificity 0.98). Finally, a model for the synchronous prediction of histology and anatomical disease origin resulted in high accuracy in determining gastroesophageal AC (sensitivity 0.88; specificity 0.92), pulmonary AC (sensitivity 0.89; specificity 0.88) and head and neck SCC (sensitivity 0.91; specificity 0.92). Adding PET-features was associated with marginal incremental value for both the prediction of histology and origin in the APS model. Overall, our study demonstrated a good predictive ability to determine patients' histology and anatomical origin using [18F]F-FDG-PET/CT-derived radiomics features, mainly from CT.
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Affiliation(s)
- Ricarda Hinzpeter
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Seyed Ali Mirshahvalad
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Vanessa Murad
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Lisa Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 1X6, Canada;
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Roshini Kulanthaivelu
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Andres Kohan
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Claudia Ortega
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Andrew Hope
- Department of Radiation Oncology, University Health Network, Toronto, ON M5G 2C4, Canada;
| | - Ur Metser
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
| | - Patrick Veit-Haibach
- University Medical Imaging Toronto, Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (S.A.M.); (V.M.); (R.K.); (A.K.); (C.O.); (U.M.); (P.V.-H.)
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Huang W, Son MH, Ha LN, Kang L, Cai W. More than meets the eye: 2-[ 18F]FDG PET-based radiomics predicts lymph node metastasis in colorectal cancer patients to enable precision medicine. Eur J Nucl Med Mol Imaging 2024; 51:1725-1728. [PMID: 38424238 PMCID: PMC11042987 DOI: 10.1007/s00259-024-06664-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, No.8 Xishiku Str, Xicheng District, Beijing, 100034, China
| | - Mai Hong Son
- Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam
| | - Le Ngoc Ha
- Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, No.8 Xishiku Str, Xicheng District, Beijing, 100034, China.
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin - Madison, K6/562 Clinical Science Center, 600 Highland Ave, Madison, WI, 53705-2275, USA.
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Tran K, Ginzburg D, Hong W, Attenberger U, Ko HS. Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks. Eur Radiol 2024:10.1007/s00330-024-10736-1. [PMID: 38625613 DOI: 10.1007/s00330-024-10736-1] [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: 11/13/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Lung cancer, the second most common cancer, presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers to improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts to address them with quality scoring tools. OBJECTIVE This study had two objectives: 1) identify radiomics biomarkers in post-radiotherapy stage III/IV nonsmall cell lung cancer (NSCLC) patients, 2) evaluate research quality using the CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score) frameworks, and formulate an amalgamated CLEAR-RQS tool to enhance scientific rigor. MATERIALS AND METHODS A systematic literature review (Jun-Aug 2023, MEDLINE/PubMed/SCOPUS) was conducted concerning stage III/IV NSCLC, radiotherapy, and radiomic features (RF). Extracted data included study design particulars, such as sample size, radiotherapy/CT technique, selected RFs, and endpoints. CLEAR and RQS were merged into a CLEAR-RQS checklist. Three readers appraised articles utilizing CLEAR, RQS, and CLEAR-RQS metrics. RESULTS Out of 871 articles, 11 met the inclusion/exclusion criteria. The Median cohort size was 91 (range: 10-337) with 9 studies being single-center. No common RF were identified. The merged CLEAR-RQS checklist comprised 61 items. Most unreported items were within CLEAR's "methods" and "open-source," and within RQS's "phantom-calibration," "registry-enrolled prospective-trial-design," and "cost-effective-analysis" sections. No study scored above 50% on RQS. Median CLEAR scores were 55.74% (32.33/58 points), and for RQS, 17.59% (6.3/36 points). CLEAR-RQS article ranking fell between CLEAR and RQS and aligned with CLEAR. CONCLUSION Radiomics research in post-radiotherapy stage III/IV NSCLC exhibits variability and frequently low-quality reporting. The formulated CLEAR-RQS checklist may facilitate education and holds promise for enhancing radiomics research quality. CLINICAL RELEVANCE STATEMENT Current radiomics research in the field of stage III/IV postradiotherapy NSCLC is heterogenous, lacking reproducibility, with no identified imaging biomarker. Radiomics research quality assessment tools may enhance scientific rigor and thereby facilitate radiomics translation into clinical practice. KEY POINTS There is heterogenous and low radiomics research quality in postradiotherapy stage III/IV nonsmall cell lung cancer. Barriers to reproducibility are small cohort size, nonvalidated studies, missing technical parameters, and lack of data, code, and model sharing. CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score), and the amalgamated CLEAR-RQS tool are useful frameworks for assessing radiomics research quality and may provide a valuable resource for educational purposes in the field of radiomics.
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Affiliation(s)
- Kevin Tran
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
- Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3052, Australia
| | - Daniel Ginzburg
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Wei Hong
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Hyun Soo Ko
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia.
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany.
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, 305 Grattan St, Melbourne, VIC 3000, Australia.
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Zhou W, Wen J, Huang Q, Zeng Y, Zhou Z, Zhu Y, Chen L, Guan Y, Xie F, Zhuang D, Hua T. Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners. Eur J Nucl Med Mol Imaging 2024; 51:1423-1435. [PMID: 38110710 DOI: 10.1007/s00259-023-06562-0] [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: 09/04/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023]
Abstract
PURPOSE Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with L-[methyl-11C] methionine ([11C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system. METHODS Consecutive 178 preoperative [11C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [11C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment. RESULTS The combined [11C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [11C]MET PET/CT radiomics score, patients' age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726-0.998) in the training dataset and 0.866 (95% CI, 0.777-0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541-1.000) in the training dataset and 0.766 (95% CI, 0.612-0.921) in the validation dataset. CONCLUSION [11C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.
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Affiliation(s)
- Weiyan Zhou
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianbo Wen
- Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Zhirui Zhou
- Radiation Oncology Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuhua Zhu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lei Chen
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Dongxiao Zhuang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China.
- Neurosurgical Institute of Fudan University, Shanghai, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China.
| | - Tao Hua
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China.
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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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May JL, Garcia-Mora J, Edwards M, Rossmeisl JH. An Illustrated Scoping Review of the Magnetic Resonance Imaging Characteristics of Canine and Feline Brain Tumors. Animals (Basel) 2024; 14:1044. [PMID: 38612283 PMCID: PMC11010916 DOI: 10.3390/ani14071044] [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: 02/23/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Magnetic resonance imaging (MRI) is used pervasively in veterinary practice for the antemortem diagnosis of intracranial tumors. Here, we provide an illustrated summary of the published MRI features of primary and secondary intracranial tumors of dogs and cats, following PRISMA scoping review guidelines. The PubMed and Web of Science databases were searched for relevant records, and input from stakeholders was solicited to select data for extraction. Sixty-seven studies of moderate to low-level evidence quality describing the MRI features of pathologically confirmed canine and feline brain tumors met inclusion criteria. Considerable variability in data inclusion and reporting, as well as low case numbers, prohibited comparative data analyses. Available data support a holistic MRI approach incorporating lesion number, location within the brain, shape, intrinsic signal appearances on multiparametric sequences, patterns of contrast enhancement, and associated secondary changes in the brain to prioritize differential imaging diagnoses, and often allows for accurate presumptive diagnosis of common intracranial tumors. Quantitative MRI techniques show promise for improving discrimination of neoplastic from non-neoplastic brain lesions, as well as differentiating brain tumor types and grades, but sample size limitations will likely remain a significant practical obstacle to the design of robustly powered radiomic studies. For many brain tumor variants, particularly in cats, there remains a need for standardized studies that correlate clinicopathologic and neuroimaging data.
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Affiliation(s)
- James L. May
- Veterinary and Comparative Neuro-Oncology Laboratory, Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA; (J.L.M.); (J.G.-M.)
| | - Josefa Garcia-Mora
- Veterinary and Comparative Neuro-Oncology Laboratory, Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA; (J.L.M.); (J.G.-M.)
| | - Michael Edwards
- Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA;
| | - John H. Rossmeisl
- Veterinary and Comparative Neuro-Oncology Laboratory, Department of Small Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA 24061, USA; (J.L.M.); (J.G.-M.)
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11
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Hu Y, Geng Y, Wang H, Chen H, Wang Z, Fu L, Huang B, Jiang W. Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis. J Comput Assist Tomogr 2024:00004728-990000000-00298. [PMID: 38498926 DOI: 10.1097/rct.0000000000001591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVES This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status. METHODS A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM. RESULTS Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts. CONCLUSIONS This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.
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Affiliation(s)
- Yue Hu
- From the Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - Yikang Geng
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Liaoning
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang
| | - Zekun Wang
- Department of Medical Iconography, Liaoning Cancer Hospital & Institute, Liaoning
| | - Langyuan Fu
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Bo Huang
- Department of Pathology, Liaoning Cancer Hospital and Institute, Liaoning
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning, People's Republic of China
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12
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Fang F, Wu L, Luo X, Bu H, Huang Y, Xian Wu Y, Lu Z, Li T, Yang G, Zhao Y, Weng H, Zhao J, Ma C, Li C. Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning: A multicenter study. Eur J Radiol 2024; 175:111416. [PMID: 38460443 DOI: 10.1016/j.ejrad.2024.111416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/26/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose. METHODS In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility. RESULTS Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas. CONCLUSION The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
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Affiliation(s)
- Fuxiang Fang
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Linfeng Wu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Xing Luo
- Department of Urology, Baise People's Hospital, Baise 533099, China.
| | - Huiping Bu
- Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Yueting Huang
- Department of Epidemiology and Health Statistics, School of Public Health of Guangxi Medical University, Nanning 530021, China.
| | - Yong Xian Wu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Zheng Lu
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Tianyu Li
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Guanglin Yang
- Department of Urology, Affiliated Cancer Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Yutong Zhao
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Hongchao Weng
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Jiawen Zhao
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Chenjun Ma
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
| | - Chengyang Li
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China.
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Gitto S, Annovazzi A, Nulle K, Interlenghi M, Salvatore C, Anelli V, Baldi J, Messina C, Albano D, Di Luca F, Armiraglio E, Parafioriti A, Luzzati A, Biagini R, Castiglioni I, Sconfienza LM. X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones. EBioMedicine 2024; 101:105018. [PMID: 38377797 PMCID: PMC10884340 DOI: 10.1016/j.ebiom.2024.105018] [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: 11/05/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. METHODS This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. FINDINGS Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). INTERPRETATION X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. FUNDING AIRC Investigator Grant.
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Affiliation(s)
- Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Alessio Annovazzi
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Kitija Nulle
- Radiology Department, Riga East Clinical University Hospital, Riga, Latvia
| | | | - Christian Salvatore
- DeepTrace Technologies s.r.l., Milan, Italy; Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | - Vincenzo Anelli
- Radiology and Diagnostic Imaging Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Jacopo Baldi
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Filippo Di Luca
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | | | | | | | - Roberto Biagini
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Isabella Castiglioni
- Department of Physics "G. Occhialini", Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [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: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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Dell’Aversana S, Ascione R, Vitale RA, Cavaliere F, Porcaro P, Basile L, Napolitano G, Boccalatte M, Sibilio G, Esposito G, Franzone A, Di Costanzo G, Muscogiuri G, Sironi S, Cuocolo R, Cavaglià E, Ponsiglione A, Imbriaco M. CT Coronary Angiography: Technical Approach and Atherosclerotic Plaque Characterization. J Clin Med 2023; 12:7615. [PMID: 38137684 PMCID: PMC10744060 DOI: 10.3390/jcm12247615] [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: 11/11/2023] [Revised: 12/08/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) currently represents a robust imaging technique for the detection, quantification and characterization of coronary atherosclerosis. However, CCTA remains a challenging task requiring both high spatial and temporal resolution to provide motion-free images of the coronary arteries. Several CCTA features, such as low attenuation, positive remodeling, spotty calcification, napkin-ring and high pericoronary fat attenuation index have been proved as associated to high-risk plaques. This review aims to explore the role of CCTA in the characterization of high-risk atherosclerotic plaque and the recent advancements in CCTA technologies with a focus on radiomics plaque analysis.
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Affiliation(s)
- Serena Dell’Aversana
- Department of Radiology, Santa Maria Delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy; (S.D.); (G.D.C.); (E.C.)
| | - Raffaele Ascione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Raffaella Antonia Vitale
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Fabrizia Cavaliere
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Piercarmine Porcaro
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Luigi Basile
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | | | - Marco Boccalatte
- Coronary Care Unit, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy; (M.B.); (G.S.)
| | - Gerolamo Sibilio
- Coronary Care Unit, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy; (M.B.); (G.S.)
| | - Giovanni Esposito
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Anna Franzone
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Giuseppe Di Costanzo
- Department of Radiology, Santa Maria Delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy; (S.D.); (G.D.C.); (E.C.)
| | - Giuseppe Muscogiuri
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, Piazza OMS 1, 24127 Bergamo, Italy; (G.M.); (S.S.)
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, Piazza OMS 1, 24127 Bergamo, Italy; (G.M.); (S.S.)
- School of Medicine and Surgery, University of Milano Bicocca, 20126 Milan, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy;
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria Delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy; (S.D.); (G.D.C.); (E.C.)
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (R.A.); (R.A.V.); (F.C.); (P.P.); (L.B.); (G.E.); (A.F.); (M.I.)
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16
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [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: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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17
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Wang X, Wang X, Wu T, Hu L, Xu M, Tang J, Li X, Zhong Y. Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors. Medicine (Baltimore) 2023; 102:e35690. [PMID: 38013377 PMCID: PMC10681616 DOI: 10.1097/md.0000000000035690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/27/2023] [Indexed: 11/29/2023] Open
Abstract
This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy and select features. A risk model was built to stratify low-, intermediate-, and high-risk groups. An image-defined risk factor (IDRFs) model was developed to classify 266 patients with malignant PNTs and one or more IDRFs into high-risk and non-high-risk groups. The performance of the predictive models was evaluated with respect to accuracy (Acc) and receiver operating characteristic (ROC) curve, including the area under the ROC curve (AUC). The risk model demonstrated good discrimination capability, with an area under the curve (AUC) of 0.903 to distinguish high-risk from non-high-risk groups, and 0.747 to classify intermediate- and low-risk groups. In the IDRF-based risk model with the number of IDRFs, the AUC was 0.876 for classifying the high-risk and non-high-risk groups. Radiomic analysis based on preoperative CT images has the potential to stratify the risk of pediatric malignant PNTs. It had outstanding efficiency in distinguishing patients in the high-risk group, and this predictive model of risk stratification could assist in selecting optimal aggressive treatment options.
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Affiliation(s)
- Xiaoxia Wang
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Xinrong Wang
- General Electric China Co., Ltd, Shanghai, China
| | - Tingfan Wu
- General Electric China Co., Ltd, Shanghai, China
| | - Liwei Hu
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Min Xu
- Department of Surgery, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Jingyan Tang
- Department of Hematology and Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Xin Li
- General Electric China Co., Ltd, Shanghai, China
| | - Yumin Zhong
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
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18
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Gu B, Meng M, Xu M, Feng DD, Bi L, Kim J, Song S. Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 2023; 50:3996-4009. [PMID: 37596343 PMCID: PMC10611876 DOI: 10.1007/s00259-023-06399-7] [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: 06/01/2023] [Accepted: 08/11/2023] [Indexed: 08/20/2023]
Abstract
PURPOSE Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. METHODS A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability. RESULTS Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. CONCLUSION Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.
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Affiliation(s)
- Bingxin Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China
| | - Mingyuan Meng
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Mingzhen Xu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China
| | - David Dagan Feng
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Lei Bi
- Institute of Translational Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jinman Kim
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China.
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China.
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China.
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Bouchemla F, Akchurin SV, Akchurina IV, Dyulger GP, Latynina ES, Grecheneva AV. Artificial intelligence feasibility in veterinary medicine: A systematic review. Vet World 2023; 16:2143-2149. [PMID: 38023280 PMCID: PMC10668547 DOI: 10.14202/vetworld.2023.2143-2149] [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: 06/13/2023] [Accepted: 09/20/2023] [Indexed: 12/01/2023] Open
Abstract
Background and Aim In recent years, artificial intelligence (AI) has become increasingly necessary in the life sciences, particularly medicine and healthcare. This study aimed to systematically review the literature and critically analyze multiple databases on the use of AI in veterinary medicine to assess its challenges. We aim to foster an understanding of the effects that can be approached and applied for professional awareness. Materials and Methods This study used multiple electronic databases with information on applied AI in veterinary medicine based on the current guidelines outlined in PRISMA and Cochrane for systematic review. The electronic databases PubMed, Embase, Google Scholar, Cochrane Library, and Elsevier were thoroughly screened through March 22, 2023. The study design was carefully chosen to emphasize evidence quality and population heterogeneity. Results A total of 385 of the 883 citations initially obtained were thoroughly reviewed. There were four main areas that AI addressed; the first was diagnostic issues, the second was education, animal production, and epidemiology, the third was animal health and welfare, pathology, and microbiology, and the last was all other categories. The quality assessment of the included studies found that they varied in their relative quality and risk of bias. However, AI aftereffect-linked algorithms have raised criticism of their generated conclusions. Conclusion Quality assessment noted areas of AI outperformance, but there was criticism of its performance as well. It is recommended that the extent of AI in veterinary medicine should be increased, but it should not take over the profession. The concept of ambient clinical intelligence is adaptive, sensitive, and responsive to the digital environment and may be attractive to veterinary professionals as a means of lowering the fear of automating veterinary medicine. Future studies should focus on an AI model with flexible data input, which can be expanded by clinicians/users to maximize their interaction with good algorithms and reduce any errors generated by the process.
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Affiliation(s)
- Fayssal Bouchemla
- Department of Animal Disease, Veterinarian and Sanitarian Expertise, Faculty of Veterinary Medicine, Vavilov Saratov State University of Genetic, Biotechnology and Engineering Saratov, Russia
| | - Sergey Vladimirovich Akchurin
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Irina Vladimirovna Akchurina
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Georgiy Petrovitch Dyulger
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Evgenia Sergeevna Latynina
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Anastasia Vladimirovna Grecheneva
- Department of Applied Informatics, Russian State Agrarian University-Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
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20
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Wang M, Mei T, Gong Y. The quality and clinical translation of radiomics studies based on MRI for predicting Ki-67 levels in patients with breast cancer. Br J Radiol 2023; 96:20230172. [PMID: 37724784 PMCID: PMC10546437 DOI: 10.1259/bjr.20230172] [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: 02/17/2023] [Revised: 05/13/2023] [Accepted: 08/02/2023] [Indexed: 09/21/2023] Open
Abstract
OBJECTIVE To evaluate the methodological quality of radiomics literature predicting Ki-67 levels based on MRI in patients with breast cancer (BC) and to propose suggestions for clinical translation. METHODS In this review, we searched PubMed, Embase, and Web of Science for studies published on radiomics in patients with BC. We evaluated the methodological quality of the studies using the Radiomics Quality Score (RQS). The Cochrane Collaboration's software (RevMan 5.4), Meta-DiSc (v. 1.4) and IBM SPSS (v. 26.0) were used for all statistical analyses. RESULTS Eighteen studies met our inclusion criteria, and the average RQS was 10.17 (standard deviation [SD]: 3.54). None of these studies incorporated any of the following items: a phantom study on all scanners, cut-off analyses, prospective study, cost-effectiveness analysis, or open science and data. In the meta-analysis, it showed apparent diffusion coefficient (ADC) played a better role to predict Ki-67 level than dynamic contrast-enhanced (DCE) MRI in the radiomics, with the pooled area under the curve (AUC) of 0.969. CONCLUSION Ki-67 index is a common tumor biomarker with high clinical value. Radiomics is an ever-growing quantitative data-mining method helping predict tumor biomarkers from medical images. However, the quality of the reviewed studies evaluated by the RQS was not so satisfactory and there are ample opportunities for improvement. Open science and data, external validation, phantom study, publicly open radiomics database and standardization in the radiomics practice are what researchers should pay more attention to in the future. ADVANCES IN KNOWLEDGE The RQS tool considered the radiomics used to predict the Ki-67 level was of poor quality. ADC performed better than DCE in radiomic prediction. We propose some measures to facilitate the clinical translation of radiomics.
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Affiliation(s)
- Min Wang
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Mei
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Youling Gong
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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21
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Laino ME, Fiz F, Morandini P, Costa G, Maffia F, Giuffrida M, Pecorella I, Gionso M, Wheeler DR, Cambiaghi M, Saba L, Sollini M, Chiti A, Savevsky V, Torzilli G, Viganò L. A virtual biopsy of liver parenchyma to predict the outcome of liver resection. Updates Surg 2023; 75:1519-1531. [PMID: 37017906 DOI: 10.1007/s13304-023-01495-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/20/2023] [Indexed: 04/06/2023]
Abstract
The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females-median age 67 years-39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Pierandrea Morandini
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Fiore Maffia
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy
| | - Mario Giuffrida
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Matteo Gionso
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Dakota Russell Wheeler
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Martina Cambiaghi
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Victor Savevsky
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy.
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Viale Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General and Oncologic Surgery, Humanitas Gavazzeni University Hospital, Viale M. Gavazzeni 21, 24125, Bergamo, Italy.
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van Voorst H, Bruggeman AAE, Yang W, Andriessen J, Welberg E, Dutra BG, Konduri PR, Arrarte Terreros N, Hoving JW, Tolhuisen ML, Kappelhof M, Brouwer J, Boodt N, van Kranendonk KR, Koopman MS, Hund HM, Krietemeijer M, van Zwam WH, van Beusekom HMM, van der Lugt A, Emmer BJ, Marquering HA, Roos YBWEM, Caan MWA, Majoie CBLM. Thrombus radiomics in patients with anterior circulation acute ischemic stroke undergoing endovascular treatment. J Neurointerv Surg 2023; 15:e79-e85. [PMID: 35882552 DOI: 10.1136/jnis-2022-019085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 07/10/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Thrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated patients with acute ischemic stroke. METHODS Thrombi were segmented on thin-slice non-contrast CT (≤1 mm) from 699 patients included in the MR CLEAN Registry. In a pilot study, we selected 51 TR with consistent values across two raters' segmentations (ICC >0.75). Random forest models using TR in addition or as a substitute to baseline clinical variables (CV) and manual thrombus measurements (MTM) were trained with 499 patients and evaluated on 200 patients for predicting successful reperfusion (extended Thrombolysis in Cerebral Ischemia (eTICI) ≥2B), first attempt reperfusion, reperfusion within three attempts, and functional independence (modified Rankin Scale (mRS) ≤2). Three texture and shape features were selected based on feature importance and related to eTICI ≥2B, number of attempts to eTICI ≥2B, and 90-day mRS with ordinal logistic regression. RESULTS Random forest models using TR, CV or MTM had comparable predictive performance. Thrombus texture (inverse difference moment normalized) was independently associated with reperfusion (adjusted common OR (acOR) 0.85, 95% CI 0.72 to 0.99). Thrombus volume and texture were also independently associated with the number of attempts to successful reperfusion (acOR 1.36, 95% CI 1.03 to 1.88 and acOR 1.24, 95% CI 1.04 to 1.49). CONCLUSIONS TR describing thrombus volume and texture were associated with more attempts to successful reperfusion. Compared with models using CV and MTM, TR had no added value for predicting procedural and functional outcome.
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Affiliation(s)
- Henk van Voorst
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Agnetha A E Bruggeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Wenjin Yang
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Neurosurgery, Shanghai Pudong New Area People's Hospital, Shanghai, China
| | - Jurr Andriessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Elise Welberg
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Bruna G Dutra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Praneeta R Konduri
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nerea Arrarte Terreros
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Jan W Hoving
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon L Tolhuisen
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon Kappelhof
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Josje Brouwer
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nikki Boodt
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Katinka R van Kranendonk
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Miou S Koopman
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Hajo M Hund
- Department of Radiology and Nuclear Medicine, Haaglanden Medical Center Bronovo, Den Haag, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Menno Krietemeijer
- Department of Radiology and Nuclear Medicine, Catharina Hospital, Eindhoven, The Netherlands
| | - Wim H van Zwam
- Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bart J Emmer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Yvo B W E M Roos
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Charles B L M Majoie
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
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23
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Fiz F, Rossi N, Langella S, Ruzzenente A, Serenari M, Ardito F, Cucchetti A, Gallo T, Zamboni G, Mosconi C, Boldrini L, Mirarchi M, Cirillo S, De Bellis M, Pecorella I, Russolillo N, Borzi M, Vara G, Mele C, Ercolani G, Giuliante F, Ravaioli M, Guglielmi A, Ferrero A, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model. Cancers (Basel) 2023; 15:4204. [PMID: 37686480 PMCID: PMC10486795 DOI: 10.3390/cancers15174204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023] Open
Abstract
Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical-radiomic model outperforms a purely clinical one (p < 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
| | - Noemi Rossi
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (N.R.); (F.I.)
| | - Serena Langella
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Andrea Ruzzenente
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Matteo Serenari
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (M.S.); (M.R.)
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Alessandro Cucchetti
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Teresa Gallo
- Department of Radiology, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (T.G.); (S.C.)
| | - Giulia Zamboni
- Department of Radiology, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (G.Z.); (M.B.)
| | - Cristina Mosconi
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (C.M.); (G.V.)
| | - Luca Boldrini
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy;
| | - Mariateresa Mirarchi
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Stefano Cirillo
- Department of Radiology, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (T.G.); (S.C.)
| | - Mario De Bellis
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Nadia Russolillo
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Martina Borzi
- Department of Radiology, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (G.Z.); (M.B.)
| | - Giulio Vara
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (C.M.); (G.V.)
| | - Caterina Mele
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Giorgio Ercolani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
- Department of General Surgery, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy;
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (F.A.); (C.M.); (F.G.)
| | - Matteo Ravaioli
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, Italy; (M.S.); (M.R.)
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy; (A.C.); (G.E.)
| | - Alfredo Guglielmi
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, University Hospital G.B. Rossi, 37134 Verona, Italy; (A.R.); (M.D.B.); (A.G.)
| | - Alessandro Ferrero
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy; (S.L.); (N.R.); (A.F.)
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Milan, Italy; (F.F.); (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (N.R.); (F.I.)
- CHDS—Center for Health Data Science, Human Technopole, 20157 Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (I.P.); (G.T.)
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, 24125 Bergamo, Italy
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24
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Courlet P, Abler D, Guidi M, Girard P, Amato F, Vietti Violi N, Dietz M, Guignard N, Wicky A, Latifyan S, De Micheli R, Jreige M, Dromain C, Csajka C, Prior JO, Venkatakrishnan K, Michielin O, Cuendet MA, Terranova N. Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy. CPT Pharmacometrics Syst Pharmacol 2023; 12:1170-1181. [PMID: 37328961 PMCID: PMC10431051 DOI: 10.1002/psp4.12983] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 06/18/2023] Open
Abstract
The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.
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Affiliation(s)
- Perrine Courlet
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Daniel Abler
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Institute of Informatics, School of Management, University of Applied Sciences Western Switzerland (HES‐SO)SierreSwitzerland
| | - Monia Guidi
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- Service of Clinical PharmacologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Pascal Girard
- Merck Institute of Pharmacometrics, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany)LausanneSwitzerland
| | - Federico Amato
- Swiss Data Science Centre, École Polytechnique Fédérale de Lausanne (EPFL) and Eidgenössische Technische Hochschule Zurich (ETH)ZurichSwitzerland
| | - Naik Vietti Violi
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Matthieu Dietz
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Nicolas Guignard
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Alexandre Wicky
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Sofiya Latifyan
- Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Rita De Micheli
- Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Mario Jreige
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Clarisse Dromain
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Chantal Csajka
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaUniversity of LausanneGenevaSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
| | - John O. Prior
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Olivier Michielin
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Michel A. Cuendet
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Swiss Institute of Bioinformatics, University of LausanneLausanneSwitzerland
- Department of Physiology and Biophysics, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Nadia Terranova
- Merck Institute of Pharmacometrics, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany)LausanneSwitzerland
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25
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Yang Z, Gong J, Li J, Sun H, Pan Y, Zhao L. The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis. Int J Surg 2023; 109:2451-2466. [PMID: 37463039 PMCID: PMC10442126 DOI: 10.1097/js9.0000000000000441] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/01/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. MATERIALS AND METHODS PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. RESULTS Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. CONCLUSIONS Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.
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Affiliation(s)
- Zhi Yang
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Gong
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital
| | - Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital
| | - Yanglin Pan
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Air Force Medical University, Xi’an, People’s Republic of China
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital
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26
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Chirica C, Haba D, Cojocaru E, Mazga AI, Eva L, Dobrovat BI, Chirica SI, Stirban I, Rotundu A, Leon MM. One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging. Life (Basel) 2023; 13:1561. [PMID: 37511936 PMCID: PMC10381280 DOI: 10.3390/life13071561] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/07/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.
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Affiliation(s)
- Costin Chirica
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Danisia Haba
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Elena Cojocaru
- Department of Morphofunctional Sciences I, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Andreea Isabela Mazga
- Faculty of General Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Lucian Eva
- Department of Anatomy, Apollonia University, 11 Pacurari Str., 700535 Iasi, Romania
| | - Bogdan Ionut Dobrovat
- Department of Radiology, Emergency Hospital Professor Doctor Nicolae Oblu, 700309 Iasi, Romania
| | - Sabina Ioana Chirica
- Faculty of General Medicine, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ioana Stirban
- Department of Neurosurgery, Emergency Hospital Professor Doctor Nicolae Oblu, 700309 Iasi, Romania
| | - Andreea Rotundu
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Maria Magdalena Leon
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
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27
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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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28
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Zhong J, Xing Y, Zhang G, Hu Y, Ding D, Ge X, Pan Z, Yin Q, Zhang H, Yang Q, Zhang H, Yao W. A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers. J Orthop Surg Res 2023; 18:414. [PMID: 37287036 DOI: 10.1186/s13018-023-03863-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/16/2023] [Indexed: 06/09/2023] Open
Abstract
PURPOSE To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. METHODS We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented. RESULTS Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present. CONCLUSION The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.
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Affiliation(s)
- Jingyu Zhong
- 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
| | - Guangcheng Zhang
- Department of Sports Medicine, 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
| | - 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
| | - Zhen Pan
- Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Huizhen Zhang
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Qingcheng Yang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University 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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Costa G, Cavinato L, Fiz F, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging 2023; 36:1038-1048. [PMID: 36849835 PMCID: PMC10287605 DOI: 10.1007/s10278-023-00799-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/01/2023] Open
Abstract
Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.
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Affiliation(s)
- Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Lara Cavinato
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy.
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, Via M. Gavazzeni 21, 24125, Bergamo, Italy.
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Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
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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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Abler D, Schaer R, Oreiller V, Verma H, Reichenbach J, Aidonopoulos O, Evéquoz F, Jreige M, Prior JO, Depeursinge A. QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research. Eur Radiol Exp 2023; 7:16. [PMID: 36947346 PMCID: PMC10033788 DOI: 10.1186/s41747-023-00326-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: 09/22/2022] [Accepted: 01/23/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. METHODS We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. RESULTS Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data. CONCLUSIONS QI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/ . Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform.
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Affiliation(s)
- Daniel Abler
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
- Department of Oncology, Precision Oncology Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Roger Schaer
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Valentin Oreiller
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Himanshu Verma
- Knowledge and Intelligence Design Group, Delft University of Technology, Delft, The Netherlands
| | - Julien Reichenbach
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
| | - Orfeas Aidonopoulos
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
| | - Florian Evéquoz
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Adrien Depeursinge
- Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Househ M, Sheikh J. The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review. J Med Internet Res 2023; 25:e40259. [PMID: 36917147 PMCID: PMC10131991 DOI: 10.2196/40259] [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: 06/13/2022] [Revised: 08/23/2022] [Accepted: 01/21/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction. OBJECTIVE The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels. METHODS We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices. CONCLUSIONS This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future. TRIAL REGISTRATION PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc.
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Affiliation(s)
- Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, Tsoumpas C. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules 2023; 13:343. [PMID: 36830712 PMCID: PMC9953018 DOI: 10.3390/biom13020343] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
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Affiliation(s)
- Lisa M. Duff
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew F. Scarsbrook
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Nishant Ravikumar
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds LS2 9JT, UK
| | - Russell Frood
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Sarah L. Mackie
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Marc A. Bailey
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds LS2 9NS, UK
| | - Jason M. Tarkin
- Division of Cardiovascular Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Justin C. Mason
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Kornelis S. M. van der Geest
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ann W. Morgan
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Charalampos Tsoumpas
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
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Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer. Abdom Radiol (NY) 2023; 48:510-518. [PMID: 36418614 DOI: 10.1007/s00261-022-03738-4] [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: 04/26/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Precise preoperative prediction of lymph node metastasis (LNM) is crucial for optimal diagnosis and treatment in patients with gastric cancer (GC), in which existing imaging methods have certain limitations. We hypothesized that PET primary lesion-based radiomics signature could provide incremental value to conventional metabolic parameters and traditional risk indicators in predicting LNM in patients with GC. METHODS This retrospective study was performed in 127 patients with GC who underwent preoperative PET/CT. Basic clinical data and PET conventional metabolic parameters were collected. Radiomics signature was constructed by the least absolute shrinkage and selection operator algorithm (LASSO) logistic regression. Based on the postoperative histological results, the patients were divided into LNM group and non-lymph node metastasis (NLNM) group. Receiver-operating characteristic (ROC) was used to evaluate the discriminatory ability of Radiomics score (Rad-score) for predicting LNM and determine whether adding Rad-score to PET conventional metabolic parameters and traditional risk factors could improve the predictive value in LNM. The Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to further confirm the incremental value of Rad-score for predicting LNM in GC. RESULTS The LNM group had higher Rad-score than NLNM group [(0.35 (-0.13-0.85) vs. -0.61 (-1.92-0.18), P < 0 .001)]. After adjusted for gender, age, BMI, and FBG, multivariable logistic regression analysis illustrated that Rad-score (OR: 6.38, 95% CI: 2.73-14.91, P < 0.0001) was independent risk factors for LNM in GC. Adding PET conventional parameters to traditional risk factors increased the predictive value of LNM in GC (AUC 0.751 vs 0.651, P = 0.02). Additional inclusion of Rad-score to conventional metabolic parameters and traditional risk indicators significantly improved the AUC (0.882 vs 0.751; P = 0.006). Bootstrap resampling (times = 500) was used for internal verification, 95% confidence interval (CI) was 0.802-0.948, with the sensitivity equaled to 89.5%, and positive predictive value (PPV) was 93.5%. When Rad-score was added to conventional metabolic parameters and traditional risk indicators, net reclassification improvement (NRI) was 0.293 (P = 0.0040) and integrated discrimination improvement (IDI) was 0.293 (P = 0.0045). CONCLUSION In GC patients, PET Radiomics signature of the primary lesion-based was significantly associated with LNM and could improve the prediction of LNM above PET conventional metabolic parameters and traditional risk factors, which could provide incremental value for individual diagnosis and treatment of GC.
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [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: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Zhong J, Hu Y, Ge X, Xing Y, Ding D, Zhang G, Zhang H, Yang Q, Yao W. A systematic review of radiomics in chondrosarcoma: assessment of study quality and clinical value needs handy tools. Eur Radiol 2023; 33:1433-1444. [PMID: 36018355 DOI: 10.1007/s00330-022-09060-3] [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: 02/21/2022] [Revised: 06/24/2022] [Accepted: 07/24/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate the study quality and clinical value of radiomics studies on chondrosarcoma. METHODS PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched for articles on radiomics for evaluating chondrosarcoma as of January 31, 2022. The study quality was assessed according to Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, Image Biomarker Standardization Initiative (IBSI) guideline, and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The level of evidence supporting clinical use of radiomics on chondrosarcoma differential diagnosis was determined based on meta-analyses. RESULTS Twelve articles were included. The median RQS was 10.5 (range, -3 to 15), with an adherence rate of 36%. The adherence rate was extremely low in domains of high-level evidence (0%), open science and data (17%), and imaging and segmentation (35%). The adherence rate of the TRIPOD checklist was 61%, and low for section of title and abstract (13%), introduction (42%), and results (56%). The reporting rate of pre-processing steps according to the IBSI guideline was 60%. The risk of bias and concern of application were mainly related to the index test. The meta-analysis on differential diagnosis of enchondromas vs. chondrosarcomas showed a diagnostic odds ratio of 43.90 (95% confidential interval, 25.33-76.10), which was rated as weak evidence. CONCLUSIONS The current scientific and reporting quality of radiomics studies on chondrosarcoma was insufficient. Radiomics has potential in facilitating the optimization of operation decision-making in chondrosarcoma. KEY POINTS • Among radiomics studies on chondrosarcoma, although differential diagnostic models showed promising performance, only pieces of weak level of evidence were reached with insufficient study quality. • Since the RQS rating, the TRIPOD checklist, and the IBSI guideline have largely overlapped with each other, it is necessary to establish one widely acceptable methodological and reporting guideline for radiomics research. • The TRIPOD model typing, the phase classification of image mining studies, and the level of evidence category are useful tools to assess the gap between academic research and clinical application, although their modifications for radiomics studies are needed.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China
| | - Guangcheng Zhang
- Department of Sports Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Qingcheng Yang
- Department of Orthopedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, 200336, China.
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Radiomics-Based Inter-Lesion Relation Network to Describe [ 18F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer. Cancers (Basel) 2023; 15:cancers15030823. [PMID: 36765781 PMCID: PMC9913254 DOI: 10.3390/cancers15030823] [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: 12/27/2022] [Revised: 01/17/2023] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each "unit" (lesion) as a part of the "entire" (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic profile of lesions extracted from [18F]FMCH PET/CT, ii) qualitative assessment of intra-tumor heterogeneity of patients, iii) quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions' profiles, to be associated with prognostic factors. We confirmed that metastatic prostate cancer patients encompassed lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome.
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Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction. Cancers (Basel) 2023; 15:cancers15030673. [PMID: 36765628 PMCID: PMC9913206 DOI: 10.3390/cancers15030673] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.
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Jiang YW, Xu XJ, Wang R, Chen CM. Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis. Front Oncol 2023; 12:973104. [PMID: 36703784 PMCID: PMC9872032 DOI: 10.3389/fonc.2022.973104] [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: 06/20/2022] [Accepted: 12/20/2022] [Indexed: 01/11/2023] Open
Abstract
Background This meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage. Methods Throughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA). Results Ten articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers. Conclusions The NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].
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Ninatti G, Pini C, Gelardi F, Sollini M, Chiti A. The Role of PET Imaging in the Differential Diagnosis between Radiation Necrosis and Recurrent Disease in Irradiated Adult-Type Diffuse Gliomas: A Systematic Review. Cancers (Basel) 2023; 15:cancers15020364. [PMID: 36672314 PMCID: PMC9856914 DOI: 10.3390/cancers15020364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
Adult-type diffuse gliomas are treated with a multimodality treatment approach that includes radiotherapy both in the primary setting, and in the case of progressive or recurrent disease. Radiation necrosis represents a major complication of radiotherapy. Recurrent disease and treatment-related changes are often indistinguishable using conventional imaging methods. The present systematic review aims at assessing the diagnostic role of PET imaging using different radiopharmaceuticals in differentiating radiation necrosis and disease relapse in irradiated adult-type diffuse gliomas. We conducted a comprehensive literature search using the PubMed/MEDLINE and EMBASE databases for original research studies of interest. In total, 436 articles were assessed for eligibility. Ten original papers, published between 2014 and 2022, were selected. Four articles focused on [18F]FDG, seven on amino acid tracers ([18F]FET n = 3 and [11C]MET n = 4), one on [11C]CHO, and one on [68Ga]Ga-PSMA. Visual assessment, semi-quantitative methods, and radiomics were applied for image analysis. Furthermore, 2/10 papers were comparative studies investigating different radiopharmaceuticals. The present review, the first one on the topic in light of the new 2021 CNS WHO classification, highlighted the usefulness of PET imaging in distinguishing radiation necrosis and tumour recurrence, but revealed high heterogeneity among studies.
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Affiliation(s)
- Gaia Ninatti
- Residency Program in Nuclear Medicine, School of Medicine and Surgery, University of Milano Bicocca, 20900 Monza, Italy
| | - Cristiano Pini
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Humanitas Research Hospital, Department of Nuclear Medicine, Via Manzoni 56, 20089 Rozzano, Italy
| | - Fabrizia Gelardi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Humanitas Research Hospital, Department of Nuclear Medicine, Via Manzoni 56, 20089 Rozzano, Italy
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Humanitas Research Hospital, Department of Nuclear Medicine, Via Manzoni 56, 20089 Rozzano, Italy
- Correspondence: ; Tel.: +39-0282245614
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Humanitas Research Hospital, Department of Nuclear Medicine, Via Manzoni 56, 20089 Rozzano, Italy
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Ponsiglione A, Stanzione A, Spadarella G, Baran A, Cappellini LA, Lipman KG, Van Ooijen P, Cuocolo R. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol 2023; 33:2239-2247. [PMID: 36303093 PMCID: PMC9935717 DOI: 10.1007/s00330-022-09180-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/26/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. METHODS Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. RESULTS From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. CONCLUSIONS The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. KEY POINTS • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Agah Baran
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | | | - Kevin Groot Lipman
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, Groningen, the Netherlands
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Hu Q, Li K, Yang C, Wang Y, Huang R, Gu M, Xiao Y, Huang Y, Chen L. The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges. Front Oncol 2023; 13:1133164. [PMID: 36959810 PMCID: PMC10028142 DOI: 10.3389/fonc.2023.1133164] [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: 12/28/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC). Materials and methods A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis. Results Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability. Conclusion AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.
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Affiliation(s)
- Qiuyuan Hu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Ke Li
- Department of Cancer Biotherapy Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Conghui Yang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yue Wang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Rong Huang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Mingqiu Gu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yuqiang Xiao
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yunchao Huang
- Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
| | - Long Chen
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A 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
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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Shi Y, Wei N, Wang K, Wu J, Tao T, Li N, Lv B. Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy. Front Oncol 2023; 13:1122247. [PMID: 36950553 PMCID: PMC10025314 DOI: 10.3389/fonc.2023.1122247] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
Background Chronic atrophic gastritis (CAG) is a precancerous condition. It is not easy to detect CAG in endoscopy. Improving the detection rate of CAG under endoscopy is essential to reduce or interrupt the occurrence of gastric cancer. This study aimed to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the CAG detection rate during endoscopy. Methods We collected 10,961 endoscopic images and 118 video clips from 4,050 patients. For model training and testing, we divided them into two groups based on the pathological results: CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) DL networks for CAG recognition and selected one of them for further improvement. The improved network was called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps. Results After fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.5% in the external test set and 96.23%, 89.23%, and 92.37% in the video test set, respectively, which were higher than those of the three endoscopists. Conclusions The CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.
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Affiliation(s)
- Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Ning Wei
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Kunhong Wang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Jingjing Wu
- Department of Internal Medicine, Zhangdian Maternal and Child Health Care Hospital, Zibo, Shandong, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Na Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
- *Correspondence: Bing Lv, ; Na Li,
| | - Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China
- *Correspondence: Bing Lv, ; Na Li,
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Clinical-Radiomics Nomogram from T1W, T1CE, and T2FS MRI for Improving Diagnosis of Soft-Tissue Sarcoma. Mol Imaging Biol 2022; 24:995-1006. [PMID: 35799035 DOI: 10.1007/s11307-022-01751-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: 03/22/2022] [Revised: 06/02/2022] [Accepted: 06/16/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE To compare values of multiparametric magnetic resonance imaging (MRI) sequences and propose clinical-radiomics nomogram for diagnosis of soft-tissue sarcoma (STS). PROCEDURES This study enrolled 148 patients from Dec. 2017 to Feb. 2021. All patients underwent T1-weighted (T1W), contrast-enhanced T1-weighted (T1CE), and T2-weighted fat-suppressed (T2FS) MRI scans. A total of 1967 radiomic features were extracted from the segmented regions of interest (ROIs) in each MRI sequence. Highly diagnostic radiomic features were selected with Mann-Whitney U test, elastic net, and Akaike's information criterion (AIC) based on MRI images. Logistical regression was used to build Rad scores. Clinical factors were analyzed using the chi-square test or Mann-Whitney U test. The performance of the Rad scores was judged using the area under the receiver operating characteristic area under the curve (ROC AUC), sensitivity, specificity, and accuracy. The nomogram was developed by integrating the Rad score and the most important clinical factor. RESULTS By combining the three MRI sequences, the Rad-Com was developed consisting of twelve features selected by with Mann-Whitney U test, elastic net, and AIC: four from T1W, three from TICE, and five from T2FS MRI. The margin (P < 0.05) demonstrated a statistically significant difference between patients with benign and malignant soft-tissue tumors (STT). The nomogram was constructed by integrating the Rad-Com and margin, which yielded favorable diagnostic AUCs of 0.919 (sensitivity (Sen) = 0.784, specificity (Spe) = 0.936) and 0.913 (Sen = 0.923, Spe = 0.792) in the training and validation cohort. CONCLUSION The proposed nomogram may have potential as a noninvasive marker for STS diagnosis.
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Basran PS, Porter I. Radiomics in veterinary medicine: Overview, methods, and applications. Vet Radiol Ultrasound 2022; 63 Suppl 1:828-839. [PMID: 36514226 DOI: 10.1111/vru.13156] [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: 07/01/2021] [Revised: 09/24/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
Radiomics, or quantitative image analysis from radiographic image data, borrows the suffix from other emerging -omics fields of study, such as genomics, proteomics, and metabolomics. This report provides an overview of the general principles of how radiomic features are computed, describes major types of morphological, first order, and texture features, and the applications, challenges, and opportunities of radiomics as applied in veterinary medicine. Some advantages radiomics has over traditional semantic radiological features include standardized methodology in computing semantic features, the ability to compute features in multi-dimensional images, their newfound associations with genomic and pathological abnormalities, and the number of perceptible and imperceptible features available for regression or classification modeling. Some challenges in deploying radiomics in a clinical setting include sensitivity to image acquisition settings and image artifacts, pre- and post-image reconstruction and calculation settings, variability in feature estimates stemming from inter- and intra-observer contouring errors, and challenges with software and data harmonization and generalizability of findings given the challenges of small sample size and patient selection bias in veterinary medicine. Despite this, radiomics has enormous potential in patient-centric diagnostics, prognosis, and theragnostics. Fully leveraging the utility of radiomics in veterinary medicine will require inter-institutional collaborations, data harmonization, and data sharing strategies amongst institutions, transparent and robust model development, and multi-disciplinary efforts within and outside the veterinary medical imaging community.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Ian Porter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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Chalkidou A, Shokraneh F, Kijauskaite G, Taylor-Phillips S, Halligan S, Wilkinson L, Glocker B, Garrett P, Denniston AK, Mackie A, Seedat F. Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. Lancet Digit Health 2022; 4:e899-e905. [PMID: 36427951 DOI: 10.1016/s2589-7500(22)00186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 08/11/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022]
Abstract
Rigorous evaluation of artificial intelligence (AI) systems for image classification is essential before deployment into health-care settings, such as screening programmes, so that adoption is effective and safe. A key step in the evaluation process is the external validation of diagnostic performance using a test set of images. We conducted a rapid literature review on methods to develop test sets, published from 2012 to 2020, in English. Using thematic analysis, we mapped themes and coded the principles using the Population, Intervention, and Comparator or Reference standard, Outcome, and Study design framework. A group of screening and AI experts assessed the evidence-based principles for completeness and provided further considerations. From the final 15 principles recommended here, five affect population, one intervention, two comparator, one reference standard, and one both reference standard and comparator. Finally, four are appliable to outcome and one to study design. Principles from the literature were useful to address biases from AI; however, they did not account for screening specific biases, which we now incorporate. The principles set out here should be used to support the development and use of test sets for studies that assess the accuracy of AI within screening programmes, to ensure they are fit for purpose and minimise bias.
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Affiliation(s)
| | - Farhad Shokraneh
- King's Technology Evaluation Centre, King's College London, London, UK
| | - Goda Kijauskaite
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | | | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | | | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Peter Garrett
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK
| | - Alastair K Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Farah Seedat
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
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[ 18F]-Fludeoxyglucose Positron Emission Tomography/Computed Tomography with Radiomics Analysis in Patients Undergoing Aortic In-Situ Reconstruction with Cryopreserved Allografts. Diagnostics (Basel) 2022; 12:diagnostics12112831. [PMID: 36428890 PMCID: PMC9689434 DOI: 10.3390/diagnostics12112831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/28/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The aim of this study was to evaluate the effectiveness of positron emission tomography/computed tomography with [18F]-fludeoxyglucose (FDG-PET/CT) and radiomics analysis in detecting differences between the native aorta and the abdominal aortic allograft after the total eradication of infection in patients undergoing infected graft removal and in situ reconstruction with cryopreserved allografts. METHODS Between January 2008 and December 2018, 56 vascular reconstructions with allografts have been performed at our department. The present series included 12 patients undergoing abdominal aortic in situ reconstruction with cryopreserved allografts. During the follow-up, all patients underwent a total-body [18F]FDG PET/CT with subsequent radiomics analysis. In all patients, a comparative analysis between the data extracted from native aorta and cryopreserved graft for each patient was performed. RESULTS All patients were male with a mean age of 72.8 years (range 63-84). Mean duration of follow-up was 51.3 months (range 3-120). During the follow-up, 2 patients (16.7%) needed a redo allograft-related surgical intervention. Overall, the rate of allograft dilatation was 33.3%. No patient had a redo infection during the follow-up. Radiomics analysis showed a different signature of implanted allograft and native aorta. Comparative analysis between the native aortas and cryopreserved allografts (dilated or not) showed several statistical differences for many texture features. CONCLUSIONS The higher metabolic activity of allografts could indicate a state of immune-mediated degeneration. This theory should be proven with prospective, multicentric studies with larger sample sizes.
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