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Marinkovic M, Stojanovic-Rundic S, Stanojevic A, Tomasevic A, Jankovic R, Zoidakis J, Castellví-Bel S, Fijneman RJA, Cavic M, Radulovic M. Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy Prediction. J Clin Med 2024; 13:421. [PMID: 38256556 PMCID: PMC10816962 DOI: 10.3390/jcm13020421] [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: 10/27/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 01/24/2024] Open
Abstract
(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma.
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Affiliation(s)
- Mladen Marinkovic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Suzana Stojanovic-Rundic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Aleksandra Stanojevic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Aleksandar Tomasevic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Radmila Jankovic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Jerome Zoidakis
- Department of Biotechnology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece;
- Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Sergi Castellví-Bel
- Gastroenterology Deparment, Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Clínic Barcelona, University of Barcelona, 08036 Barcelona, Spain;
| | - Remond J. A. Fijneman
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
| | - Milena Cavic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
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Volpe S, Gaeta A, Colombo F, Zaffaroni M, Mastroleo F, Vincini MG, Pepa M, Isaksson LJ, Turturici I, Marvaso G, Ferrari A, Cammarata G, Santamaria R, Franzetti J, Raimondi S, Botta F, Ansarin M, Gandini S, Cremonesi M, Orecchia R, Alterio D, Jereczek-Fossa BA. Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit. Cancers (Basel) 2023; 15:cancers15072022. [PMID: 37046683 PMCID: PMC10093133 DOI: 10.3390/cancers15072022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/23/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models’ performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients.
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