Computed tomography based analyses of body mass composition in HER2 positive metastatic breast cancer patients undergoing first line treatment with pertuzumab and trastuzumab

Body composition parameters (BCp) have been associated with outcome in different tumor types. However, their prognostic value in patients with HER2-positive metastatic breast cancer (BC) receiving first line treatment with dual anti-HER2 antibody blockade is unknown. Preclinical evidences suggest that adipocytes adjacent to BC cells can influence response to anti-HER2 treatments. We retrospectively analyzed Computed Tomography (CT)-based BCp from 43 patients with HER2-positive metastatic BC who received first line pertuzumab/trastuzumab-based treatment between May 2009 and March 2020. The impact of baseline CT-based BCp on progression-free survival (PFS) was tested using Kaplan-Meier estimates and univariate and multivariate Cox regression models. We found a significantly worse PFS for patients with high baseline subcutaneous fat index (median 7.9 vs 16.1 months, p = 0.047, HR = 2.04, 95%CI 1-4.17) and for those with high total abdominal fat index (8.1 vs 18.8 months, p = 0.030, HR = 2.17, 95%CI 1.06-4.46). Patients with baseline sarcopenia did not show shorter PFS compared to those without sarcopenia (10.4 vs 9.2 months, p = 0.960, HR = 0.98, 95%CI 0.47-2.03). Total abdominal fat index remained a significant predictor of PFS at multivariate analysis. Our findings suggest that a high quantity of total abdominal fat tissue is a poor prognostic factor in patients receiving trastuzumab/pertuzumab-based first-line treatment for HER2-positive metastatic BC

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147)

Breast Cancer Leptomeningeal Metastases on Spinal Epidural Lipomatosis

We present a case of breast cancer metastases superimposed on epidural lipomatosis and although none of these findings are considered rare, their coexistence leads to unique image findings, and as far as we know there are no other cases like this in literature