Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients

Abstract

Purpose: To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. Methods: Seventy-four patients (median age 63 years, range 42–86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36–86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. Results: A total of 38 patients (median age 61; range 41–78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). Conclusions: Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.

Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal

“…The "Clinical Evaluation" section collected previous examination results, a genetic panel, results of histopathological exami-nation on biopsy specimen, carbohydrate antigen 19.9 (Ca 19.9) level, carcinoembryonic antigen (CEA) level, blood count, serum creatinine, liver function, and clinical symptoms. These data could create the basis of a large database, allowing not only for the carrying out of epidemiological statistical analysis, but they could be used to build a radiomics model by combining radiological features and clinical data [33][34][35][36][37][38][39]. In this context, the added value of genomic data could be used to develop a model of radiogenomics, which was helpful regarding the highest level of personalized risk stratification and the advanced precision medicine process [40][41][42][43][44][45].…”

Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software

“…According to Johns Hopkins University, case-fatality rates of COVID-19 patients ranges between 1% and 7% based on days since first confirmed case, testing efficacy, local pandemic response policies, and the population age [45][46][47][48][49]. Multi-organ manifestations of COVID-19 are now well-documented [50][51][52][53][54][55][56][57], but the potential long-term implications of these manifestations remain to be uncovered. Several studies have reported impaired exercise capacity and diffusing capacity for carbon monoxide (DL CO ) in SARS-CoV-1 survivors extending from 6 months to 15 years of follow-up [58][59][60][61][62][63][64], suggesting impairment of the intra-alveolar diffusion pathway.…”

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

“…In oncology, the assessment of tissue heterogeneity is of particular interest; genomic analyses have demonstrated that the degree of tumor heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Studies have demonstrated that radiomics features are strongly correlated with heterogeneity indices at the cellular level [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status [ 9 , 10 , 11 , 12 , 13 , 14 ].…”

“…Even though individual features may correlate with genomic data, so-called radiogenomics, or clinical outcomes, the impact of radiomics is increased when the data are processed using machine learning techniques. Nowadays, several studies have assessed the role of radiogenomics in hepatocellular carcinoma, but only a few have examined liver metastases [ 1 , 2 , 3 ].…”