Tugba Akinci D'Antonoli1, Alessandra Farchione2, Jacopo Lenkowicz3, Marco Chiappetta4, Giuseppe Cicchetti5, Antonella Martino6, Alessandra Ottavianelli5, Riccardo Manfredi5, Stefano Margaritora7, Lorenzo Bonomo2, Vincenzo Valentini5, Anna Rita Larici5. 1. Istituto di Radiologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy; Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland. 2. Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 3. Istituto di Radiologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy. Electronic address: jacopo.lenkowicz@gmail.com. 4. Dipartimento Scienze Cardiovascolari e Chirurgiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 5. Istituto di Radiologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy; Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 6. Istituto di Radiologia, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy. 7. Dipartimento Scienze Cardiovascolari e Chirurgiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy; Istituto di Patologia Speciale Chirurgica, Università Cattolica del Sacro Cuore, Rome, Italy.
Abstract
RATIONALE AND OBJECTIVES: To estimate recurrence risk after surgery in nonsmall cell lung cancer (NSCLC) patients by employing tumoral and peritumoral radiomics analysis. MATERIALS AND METHODS: One-hundred twenty-four surgically treated stage IA-IIB NSCLC patients' data from 2008 to 2013 were retrospectively collected. Patient outcome was defined as local recurrence (LR), distant metastasis (DM), and (sum of LR and DM) total recurrence (TR) at follow-up. Volumetric region of interests (ROIs) were drawn for the tumor, peritumoral lung parenchyma (2 cm around the tumor) and involved lobe on CT images. Ninety-four (morphological, first-order, textural, fractal-based) radiomics features were extracted from the ROIs and datasets were created from single or combined ROIs. Predictive models were built with radiomics signature (RS) and clinicopathological data, and the area under the curve (AUC) was used to evaluate the performance. Radiomics score was calculated with the best models' feature coefficients, low- and high-risk groups of patients defined accordingly. Kaplan-Meier curves were built, and the log-rank test was used for comparison among low- and high-risk groups. Differences in recurrence risk among the two risk groups were calculated (chi-square test). RESULTS: Fifty-six patients developed TR (25 LR, 31 DM). The tumor-node-metastasis (TNM) stage recurrence predictability (AUCTR 0.680; AUCDM 0.672; AUCLR 0.580) was substantially improved when RS was added to the predictive model (AUCTR 0.760; AUCDM 0.759; AUCLR 0.750). Seventy-five percent of high-risk patients developed TR. Recurrence risk of the high-risk group was 16-fold higher than that of the low-risk group (p < 0.001). CONCLUSION: Combination of the tumoral and peritumoral RS with TNM staging system outperformed TNM staging alone in individualized recurrence risk estimation of patients with surgically treated NSCLC.
RATIONALE AND OBJECTIVES: To estimate recurrence risk after surgery in nonsmall cell lung cancer (NSCLC) patients by employing tumoral and peritumoral radiomics analysis. MATERIALS AND METHODS: One-hundred twenty-four surgically treated stage IA-IIB NSCLCpatients' data from 2008 to 2013 were retrospectively collected. Patient outcome was defined as local recurrence (LR), distant metastasis (DM), and (sum of LR and DM) total recurrence (TR) at follow-up. Volumetric region of interests (ROIs) were drawn for the tumor, peritumoral lung parenchyma (2 cm around the tumor) and involved lobe on CT images. Ninety-four (morphological, first-order, textural, fractal-based) radiomics features were extracted from the ROIs and datasets were created from single or combined ROIs. Predictive models were built with radiomics signature (RS) and clinicopathological data, and the area under the curve (AUC) was used to evaluate the performance. Radiomics score was calculated with the best models' feature coefficients, low- and high-risk groups of patients defined accordingly. Kaplan-Meier curves were built, and the log-rank test was used for comparison among low- and high-risk groups. Differences in recurrence risk among the two risk groups were calculated (chi-square test). RESULTS: Fifty-six patients developed TR (25 LR, 31 DM). The tumor-node-metastasis (TNM) stage recurrence predictability (AUCTR 0.680; AUCDM 0.672; AUCLR 0.580) was substantially improved when RS was added to the predictive model (AUCTR 0.760; AUCDM 0.759; AUCLR 0.750). Seventy-five percent of high-risk patients developed TR. Recurrence risk of the high-risk group was 16-fold higher than that of the low-risk group (p < 0.001). CONCLUSION: Combination of the tumoral and peritumoral RS with TNM staging system outperformed TNM staging alone in individualized recurrence risk estimation of patients with surgically treated NSCLC.
Authors: Andrea Delli Pizzi; Antonio Maria Chiarelli; Piero Chiacchiaretta; Martina d'Annibale; Pierpaolo Croce; Consuelo Rosa; Domenico Mastrodicasa; Stefano Trebeschi; Doenja Marina Johanna Lambregts; Daniele Caposiena; Francesco Lorenzo Serafini; Raffaella Basilico; Giulio Cocco; Pierluigi Di Sebastiano; Sebastiano Cinalli; Antonio Ferretti; Richard Geoffrey Wise; Domenico Genovesi; Regina G H Beets-Tan; Massimo Caulo Journal: Sci Rep Date: 2021-03-08 Impact factor: 4.996