Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68–0.78 vs 0.56–0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.     

Plasma levels of polychlorinated biphenyls (PCB) and the risk of soft tissue sarcoma

Background::Soft tissue sarcoma (STS) is a heterogeneous group of rare neoplasms whose aetiology is largely unknown. Dioxin and dioxin-like compounds, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) and polychlorinated biphenyls (PCBs), are potential risk factors for STS.Objectives:To investigate the relation of 17 PCBs congeners, assessed in human plasma, with STS risk.Methods:We conducted a case-control study in Italy, including 52 STS cases and 99 hospital-based controls. Selected PCB were extracted by high-performance liquid chromatography (HPLC) and measured with gas chromatography-mass spectrometry (GC-MS). Odds ratios (OR), and the corresponding 95% confidence intervals (CI), were estimated through multivariate logistic regression models.Results:The most frequently detected PCB congeners were 138, 170, 180 and 149 (detected in 40-77% of controls). The OR for the sum of all 17 PCB congeners was 1.20 (95% CI 0.50-2.92). In categorical analysis no consistent association was found for individual congeners and for groups based on Wolff’s classification or the degree of chlorination. For continuous estimates, borderline positive associations emerged for Wolff’s groups 2A (OR 1.23, 95% CI 0.97-1.55), 2B (OR 1.34, 95% CI 1.00-1.77, and 3 (OR 1.19, 95% CI 0.96-1.49), for moderately (OR 1.20, 95% CI 0.96-1.51) and highly (OR 1.18, 95% CI 0.99-1.41) chlorinated PCBs, and for congeners 170 (OR 1.26, 95% CI 0.98-1.63), 180 (OR 1.26, 95% CI 0.97-1.64) and 138 (OR 1.45, 95% CI 1.02-2.04).Discussion:Most associations between PCBs and STS risk were not significant, but, given the limited sample size, we cannot exclude moderate associations.Key words: Soft tissue sarcoma, polychlorinated biphenyls, epidemiology, environmental risk factors, chemical contaminants, case-control study