Prognostic significance of immunohistochemically detected breast cancer node metastases in 218 patients

Axillary lymph node metastases detected by immunohistochemistry in standard node-negative patients with breast carcinomas (13 out of 129 infiltrating ductal carcinomas and 37 out of 89 infiltrating lobular carcinomas) do not have any prognostic significance in patients followed up for a long time (respectively 24 and 18 years). Moreover, their pejorative significance in the literature is debatable since the groups and events taken into account are heterogeneous

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs

Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63-0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis

Chest Wall Resection for Adult Soft Tissue Sarcomas and Chondrosarcomas: Analysis of Prognostic Factors

Background: Wide resection with tumor-free margins is necessary in soft-tissue sarcomas to minimize local recurrence and to contribute to long-term survival. Information about treatment outcome and prognostic factors of adult sarcoma requiring chest wall resection (CWR) is limited. Methods: Sixty consecutive patients were retrospectively studied for overall survival (OS), local recurrence-free survival (LRFS), and disease-free survival (DFS). Twenty-one prognostic factors regarding survival were analyzed by univariate analysis using the Kaplan-Meier method and the log-rank test. Results: With a median survival of 2.5 years, the OS was 46% (33%) at 5 (10) years. The LRFS was 64% at 5 and 10 years, and the DFS was 30% and 25% at 5 and 10 years. At the end of the study period, 26 patients (43%) were alive, of which 20 patients (33%) had no evidence of disease and 40 patients (67%) had no chest wall recurrence. In the group of 9 patients with a radiation-induced soft-tissue sarcoma, the median survival was 8 months. Favorable outcome in univariate analysis in OS and LRFS applied for the low-grade sarcoma, bone invasion, and sternal resection. For OS only, age below 60 years and no radiotherapy were significant factors contributing to an improved survival. CWR was considered radical (R0) at the pathological examination in 43 patients. There were 52 patients with an uneventful recovery. There was one postoperative death. Conclusions: CWR for soft-tissue sarcoma is a safe surgical procedure with low morbidity and a mortality rate of less than 1%. With proper patient selection acceptable survival can be reached in a large group of patients. Care must be given to patients with radiation-induced soft-tissue sarcoma who have a significantly worse prognosis