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Wang L, Chen Y, Tan J, Ge Y, Xu Z, Wels M, Pan Z. Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study. Acta Radiol 2022; 64:1311-1321. [PMID: 36062762 DOI: 10.1177/02841851221123971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). PURPOSE To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. MATERIAL AND METHODS A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. RESULTS Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05). CONCLUSION Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jingwen Tan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yingqian Ge
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Zhihan Xu
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, 42406Siemens Healthcare GmbH, Forchheim, Germany
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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Corino VDA, Bologna M, Calareso G, Resteghini C, Sdao S, Orlandi E, Licitra L, Mainardi L, Bossi P. Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy. J Imaging 2022; 8:jimaging8020046. [PMID: 35200748 PMCID: PMC8877083 DOI: 10.3390/jimaging8020046] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/23/2022] [Accepted: 02/11/2022] [Indexed: 11/26/2022] Open
Abstract
Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. Results: The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65–0.88), 0.76 (CI: 0.62–0.87) and 0.93 (CI: 0.75–1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. Conclusions: These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.
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Affiliation(s)
- Valentina D. A. Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (M.B.); (L.M.)
- Correspondence: ; Tel.: +39-02-2399-3392
| | - Marco Bologna
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (M.B.); (L.M.)
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Carlo Resteghini
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (C.R.); (E.O.); (L.L.)
| | - Silvana Sdao
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Ester Orlandi
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (C.R.); (E.O.); (L.L.)
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (C.R.); (E.O.); (L.L.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (M.B.); (L.M.)
| | - Paolo Bossi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy;
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Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli NA, Cavallo A, Cavalieri S, Locati L, Bossi P, Romanello DA, Ingargiola R, Rancati T, Pignoli E, Sdao S, Pecorilla M, Facchinetti N, Trama A, Licitra L, Mainardi L, Orlandi E. Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers (Basel) 2020; 12:E2958. [PMID: 33066161 PMCID: PMC7601980 DOI: 10.3390/cancers12102958] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 02/06/2023] Open
Abstract
Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Giuseppina Calareso
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Chiara Tenconi
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, 20133 Milan, Italy; (C.T.); (L.L.)
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Salvatore Alfieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Nicola Alessandro Iacovelli
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
| | - Anna Cavallo
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Stefano Cavalieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Laura Locati
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Paolo Bossi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy;
| | - Domenico Attilio Romanello
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Rossana Ingargiola
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Emanuele Pignoli
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Silvana Sdao
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Mattia Pecorilla
- Post-Graduate School in Radiodiagnostics, Università degli Studi di Milano, 20133 Milan, Italy;
| | - Nadia Facchinetti
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
| | - Annalisa Trama
- Research Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy;
| | - Lisa Licitra
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, 20133 Milan, Italy; (C.T.); (L.L.)
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Ester Orlandi
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
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