Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017–06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. The pCR to NACT was determined based on the final surgical specimen. The association of clinical/biological and radiomic features with response to NACT was evaluated by univariate and multivariable analysis by using random forest and logistic regression. The performances of all models were assessed using the areas under the receiver operating characteristic curves (AUC) with 95% confidence intervals (CI). Results: Eighty-three patients (mean (SD) age, 47.26 (8.6) years) were included. Patients with HER2+, basal-like molecular subtypes and Ki67 ≥ 20% presented a pCR to NACT more frequently; the clinical/biological model’s AUC (95% CI) was 0.81 (0.71–0.90). Using 136 representative radiomics features selected through cluster analysis from the 1037 extracted features, a radiomic score was calculated to predict the response to NACT, with AUC (95% CI): 0.64 (0.51–0.75). After combining the clinical/biological and radiomics models, the AUC (95% CI) was 0.83 (0.73–0.92). Conclusions: MRI-based radiomic features slightly improved the pre-treatment prediction of pCR to NACT, in addiction to biological characteristics. If confirmed on larger cohorts, it could be helpful to identify such patients, to avoid unnecessary treatment.

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

Several studies suggest a role of gut microbiota in colorectal cancer (CRC) initiation and progression. Vitamin D (vitD) blood levels are also inversely correlated with CRC risk and prognosis. However, these factors’ interplay remains unknown. 74 CRC patients after standard treatment were randomized to 1-year 2000 IU/day vitD or placebo. Baseline and post-treatment fecal microbiota for shotgun metagenomics sequencing was collected. Coda-lasso and Principal Component Analysis were used to select and summarize treatment-associated taxa and pathways. Associations between vitD and taxa/pathways were investigated with logistic regression. Mediation analysis was performed to study if treatment-associated taxa mediated the effect of supplementation on 25(OH)D levels. Cox proportional-hazards model was used for disease-free survival (DFS). 60 patients were analyzed. Change in alpha diversity (Shannon: p = 0.77; Simpson: p = 0.63) and post-treatment beta diversity (p = 0.70) were comparable between arms. Post-treatment abundances of 63 taxa and 32 pathways differed between arms. The 63 taxa also mediated the effect of supplementation on 25(OH)D (p = 0.02). There were sex differences in vitD levels, microbiota and pathways. Pathways of essential amino acids’ biosynthesis were more abundant in supplemented women. Fusobacterium nucleatum presence at baseline was associated with worse DFS (p = 0.02). Those achieving vitD sufficiency (25(OH)D≥30 ng/ml) had lower post-treatment abundances (p = 0.05). Women were more likely to have F. nucleatum post-treatment (p = 0.02). VitD supplementation may contribute shaping the gut microbiota and the microbiota may partially mediate the effect of supplementation on 25(OH)D. The observed sex-specific differences highlight the necessity of including sex/gender as a variable in microbiome studies.

Hereditary diffuse gastric cancer (HDGC) is a complex and multifactorial inherited cancer predisposition syndrome caused by CDH1 germline mutations. Nevertheless, current CDH1 genetic screening recommendations disregard an unbalanced worldwide distribution of CDH1 variants, impacting testing efficacy and patient management. In this systematic review, we collected and analyzed all studies describing CDH1 variants in gastric cancer patients originating from both high- and low-prevalence countries. Selected studies were categorized as family study, series study, and unknown study, according to the implementation of HDGC clinical criteria for genetic testing. Our results indicate that CDH1 mutations are more frequently identified in gastric cancer low-incidence countries, and in the family study group that encompasses cases fulfilling criteria. Considering the type of CDH1 alterations, we verified that the relative frequency of mutation types varies within study groups and geographical areas. In the series study, the missense variant frequency is higher in high-incidence areas of gastric cancer, when compared with non-missense mutations. However, application of variant scoring for putative relevance led to a strong reduction of CDH1 variants conferring increased risk of gastric cancer. Herein, we demonstrate that criteria for CDH1 genetic screening are critical for identification of individuals carrying mutations with clinical significance. Further, we propose that future guidelines for testing should consider GC incidence across geographical regions for improved surveillance programs and early diagnosis of disease.