Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk.

Background: Radiomics based on computed tomography (CT) images is potential in promoting individualized treatment of non-small cell lung cancer (NSCLC), however, its role in immunotherapy needs further exploration. The aim of this study was to develop a CT-based radiomics score to predict the efficacy of immune checkpoint inhibitor (ICI) monotherapy in patients with advanced NSCLC. Methods: Two hundred and thirty-six ICI-treated patients were retrospectively included and divided into a training cohort (n=188) and testing cohort (n=48) at a ratio of 8 to 2. The efficacy outcomes of ICI were evaluated based on overall survival (OS) and progression-free survival (PFS). We designed a survival network and combined it with a Cox regression model to obtain patients' OS risk score (OSRS) and PFS risk score (PFSRS). Results: Based on OSRS and PFSRS, patients were divided into high- and low-risk groups in the training cohort and the test cohort with distinctly different [training cohort, log-rank P<0.001, hazard ratio (HR): 4.14; test cohort, log-rank P=0.014, HR: 4.54] and PFS (training cohort, log-rank P<0.001, HR: 4.52; test cohort, log-rank P<0.001, HR: 6.64). Further joint evaluation of OSRS and PFSRS showed that both were significant in the Cox regression model (P<0.001), and multi-overall survival risk score (MOSRS) displayed more outstanding stratification capabilities than OSRS in both the training (P<0.001) and test cohorts (P=0.002). None of the clinical characteristics were significant in the Cox regression model, and the score that predicted the best immune response was not as good as the risk score from follow-up information in the performance of prognostic stratification. Conclusions: We developed a CT imaging-based score with the potential to become an independent prognostic factor to screen patients who would benefit from ICI treatment, which suggested that CT radiomics could be applied for individualized immunotherapy of NSCLC. Our findings should be further validated by future larger multicenter study.

Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma.

BACKGROUND: Robust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT). METHODS: Using radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed. RESULTS: The radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P<0.001). The GSEA presented that the signature was highly correlated to characteristic metabolic process and immune system during cancer progression. Multivariable analysis revealed that age (P=0.031), pathologic TNM stage (P=0.043), histologic subtype (P=0.010) and the signature (P<0.001) were independently associated with patients' DFS. The integrated radiomics nomogram showed good discrimination performance, as well as good calibration and clinical utility, for DFS prediction in the validation cohort. We further found that the patients with high points (point ≥8.788) defined by the radiomics nomogram obtained a significant favorable response to ACT (P=0.04) while patients with low points (point <8.788) showed no survival difference (P=0.7). CONCLUSIONS: The radiomics nomogram could be used for prognostic prediction and ACT benefits identification for patient with resected stage I lung adenocarcinoma.

Primary tumour resection showed survival benefits for non-small-cell lung cancers with unexpected malignant pleural dissemination.

OBJECTIVES: Although non-small-cell lung cancer (NSCLC) with malignant pleural nodules is generally contraindicated for surgery, there is no consensus concerning on-site operative decisions for unexpected, intraoperatively encountered malignant pleural disseminations. The rationale underlying the primary tumour removal and other aggressive interventions remains controversial. METHODS: All surgical NSCLC cases (9576) of Shanghai Pulmonary Hospital between January 2005 and December 2013 were reviewed. Among them, 83 cases (0.9%) met the definition of 'unexpected' macroscopic malignant pleural nodules, despite routine preoperative evaluations for tumour metastasis. No pleural effusion was visualized in 52 cases during operations, and 31 had pleural effusion in minimal volume (<300 ml). Survivals were calculated with the Kaplan-Meier method and risk factors were evaluated by the log-rank test. RESULTS: The overall 3- and 5-year survival rates were 36.1 and 16.8%, respectively. The median survival time (MST) after surgery was significantly longer in the group without pleural effusion (37 months) compared with the group with pleural effusion (22 months, P = 0.005). Twenty-one cases had only biopsy, whereas 62 cases had primary tumour resection. Primary tumour resection had significantly better outcome compared with biopsy (MST: respectively, 35 vs 17 months, 3-year survival rate 45.8 vs 11.8%, P = 0.001). No baseline differences emerged in characteristics between biopsy and primary tumour resection groups including targeted therapy. Multivariate analysis showed that primary tumour resection (HR: 3.678, P = 0.014), no pleural effusion (HR: 3.409, P = 0.001) and adenocarcinoma (HR: 5.481, P = 0.002) were favourable prognostic factors in patients with malignant pleural nodules. CONCLUSIONS: Patients with malignant pleural nodules but without pleural effusion had better survival compared with those with effusions. Primary tumour resection had survival benefits for patients with unexpected intraoperatively proven malignant pleural nodules.