Giulia Mazzaschi1, Gianluca Milanese2, Paolo Pagano3, Denise Madeddu4, Letizia Gnetti5, Francesca Trentini6, Angela Falco7, Caterina Frati8, Bruno Lorusso9, Costanza Lagrasta10, Roberta Minari11, Luca Ampollini12, Mario Silva13, Nicola Sverzellati14, Federico Quaini15, Giovanni Roti16, Marcello Tiseo17. 1. Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: giulia.mazzaschi@studenti.unipr.it. 2. Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: gianluca.milanese@studenti.unipr.it. 3. Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: paolopagano6@gmail.com. 4. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: denise.madeddu@libero.it. 5. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: lgnetti@ao.pr.it. 6. Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: francesca.trentini@studenti.unipr.it. 7. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: angela.falco@unipr.it. 8. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: caterina.frati@unipr.it. 9. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: brunolorusso@gmail.com. 10. Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: costanzaannamaria.lagrasta@unipr.it. 11. Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: rominari@ao.pr.it. 12. Department of Medicine and Surgery, University of Parma, Thoracic Surgery, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: luca.ampollini@unipr.it. 13. Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: mario.silva@unipr.it. 14. Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: nicola.sverzellati@unipr.it. 15. Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: federico.quaini@unipr.it. 16. Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: giovanni.roti@unipr.it. 17. Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy. Electronic address: marcello.tiseo@unipr.it.
Abstract
OBJECTIVES: Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLC patients. MATERIALS AND METHODS: Our study population consisted of 100 surgically resected NSCLC patients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3high/PD-1-to-CD8low) or inert (CD8-to-CD3low/PD-1-to-CD8high) phenotypes. CT datasets acted as source for qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features which were correlated with clinico-pathological and TIME profiles to determine their impact on survival outcome. RESULTS: Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P < 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P < 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P < 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P < 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P < 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P < 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P < 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P < 0.05) in the risk assessment of NSCLC. CONCLUSION: Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.
OBJECTIVES: Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLCpatients. MATERIALS AND METHODS: Our study population consisted of 100 surgically resected NSCLCpatients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3high/PD-1-to-CD8low) or inert (CD8-to-CD3low/PD-1-to-CD8high) phenotypes. CT datasets acted as source for qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features which were correlated with clinico-pathological and TIME profiles to determine their impact on survival outcome. RESULTS: Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P < 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P < 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P < 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P < 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P < 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P < 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P < 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P < 0.05) in the risk assessment of NSCLC. CONCLUSION: Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.
Authors: T Henry; R Sun; M Lerousseau; T Estienne; C Robert; B Besse; C Robert; N Paragios; E Deutsch Journal: Sci Rep Date: 2022-10-14 Impact factor: 4.996