Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity

A Yolo-Based Model for Breast Cancer Detection in Mammograms

This work aims to implement an automated data-driven model for breast cancer detection in mammograms to support physicians' decision process within a breast cancer screening or detection program. The public available CBIS-DDSM and the INbreast datasets were used as sources to implement the transfer learning technique on full-field digital mammography proprietary dataset. The proprietary dataset reflects a real heterogeneous case study, consisting of 190 masses, 46 asymmetries, and 71 distortions. Several Yolo architectures were compared, including YoloV3, YoloV5, and YoloV5-Transformer. In addition, Eigen-CAM was implemented for model introspection and outputs explanation by highlighting all the suspicious regions of interest within the mammogram. The small YoloV5 model resulted in the best developed solution obtaining an mAP of 0.621 on proprietary dataset. The saliency maps computed via Eigen-CAM have proven capable solution reporting all regions of interest also on incorrect prediction scenarios. In particular, Eigen-CAM produces a substantial reduction in the incidence of false negatives, although accompanied by an increase in false positives. Despite the presence of hard-to-recognize anomalies such as asymmetries and distortions on the proprietary dataset, the trained model showed encouraging detection capabilities. The combination of Yolo predictions and the generated saliency maps represent two complementary outputs for the reduction of false negatives. Nevertheless, it is imperative to regard these outputs as qualitative tools that invariably necessitate clinical radiologic evaluation. In this view, the model represents a trusted predictive system to support cognitive and decision-making, encouraging its integration into real clinical practice

CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease

This study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue & mdash; around the anterior interventricular artery (IVA) & mdash; to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alone. In fact, with the best model (Random Forest + Mutual Information) the AUROC reached 0.820 +/- 0.076 . As a matter of fact, the combined use of both types of features (i.e., radiomic and clinical) allows for improved performance regardless of the feature selection method used. Experimental findings demonstrated that the use of radiomic features alone achieves better performance than the use of clinical features alone, while the combined use of both clinical and radiomic biomarkers further improves the predictive ability of the models. The main contribution of this work concerns: (i) the implementation of multimodal predictive models, based on both clinical and radiomic features, and (ii) a trusted system to support clinical decision-making processes by means of explainable classifiers and interpretable features

Transformer-Based Approach to Melanoma Detection

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948

Deregulated expression of aurora kinases is not a prognostic biomarker in papillary thyroid cancer patients.

Abstract A number of reports indicated that Aurora-A or Aurora-B overexpression represented a negative prognostic factor in several human malignancies. In thyroid cancer tissues a deregulated expression of Aurora kinases has been also demonstrated, butno information regarding its possible prognostic role in differentiated thyroid cancer is available. Here, weevaluated Aurora-A and Aurora-B mRNA expression and its prognostic relevance in a series of 87 papillary thyroid cancers (PTC), with a median follow-up of 63 months. The analysis of Aurora-A and Aurora-B mRNA levels in PTC tissues, compared to normal matched tissues, revealed that their expression was either up-or down-regulatedin the majority of cancer tissues. In particular, Aurora-A and Aurora-B mRNA levels were altered, respectively, in 55 (63.2%) and 79 (90.8%) out of the 87 PTC analyzed. A significant positive correlation between Aurora-A and Aurora-B mRNAswas observed (p=0.001). The expression of both Aurora genes was not affected by the BRAF(V600E) mutation. Univariate, multivariate and Kaplan-Mayer analyses documented the lack of association between Aurora-A or Aurora-B expression and clinicopathological parameterssuch as gender, age, tumor size, histology, TNM stage, lymph node metastasis and BRAF status as well asdisease recurrences or disease-free interval. Only Aurora-B mRNA was significantly higher in T(3-4) tissues, with respect to T(1-2) PTC tissues. The data reported here demonstrate that the expression of Aurora kinases is deregulated in the majority of PTC tissues, likely contributing to PTC progression. However, differently from other human solid cancers, detection of Aurora-A or Aurora-B mRNAs is not a prognostic biomarker inPTC patients

Perioperative chemotherapy in poorly differentiated neuroendocrine neoplasia of the bladder: A multicenter analysis

There is scant evidence about optimal management of poorly differentiated neuroendocrine carcinoma of the bladder (BNEC). We performed a multicenter retrospective study on BNEC patients from 13 Italian neuroendocrine-dedicated centers to analyze strategies associated with better outcomes. Mixed adeno-neuroendocrine carcinomas (MANEC) were included. We analyzed overall survival (OS) in the overall cohort, relapse-free survival (RFS) in radically operated patients and progression-free survival (PFS) in patients who received chemotherapy for metastatic disease. Fifty-one BNEC patients were included (male: 46, median age: 70 years). Overall, median OS was 16.0 months, radical tumor resection was performed in 37 patients (72.5%) and 11 of these (29.7%) also received peri-operative platinum-etoposide chemotherapy. Median OS was longer in patients with better performance status (PS) and in those with stage I–III disease at diagnosis compared to stage IV. Among patients who underwent radical tumor resection (N = 37), RFS was longer in patients with better PS and showed a trend towards a longer RFS in those treated with peri-operative chemotherapy than with surgery alone (11 vs. 6 months; p = 0.078). Among 28 patients receiving chemotherapy for metastatic disease, PFS was 5.0 months and there was a trend towards improved PFS in patients receiving carboplatin-etoposide chemotherapy compared to other regimens. A multivariate model unmasked a significant association between carboplatin-etoposide chemotherapy and risk for disease progression or death (HR: 0.39 (95%CI: 0.16–0.96) p = 0.040). Performance status might be associated with improved RFS in radically operated patients, while type of chemotherapy might affect PFS in patients receiving chemotherapy for metastatic BNEC

Comparative Effectiveness of Gemcitabine plus Nab-Paclitaxel and FOLFIRINOX in the First-Line Setting of Metastatic Pancreatic Cancer: A Systematic Review and Meta-Analysis

Gemcitabine and nab-paclitaxel (GEM-NAB) and the combination of 5-fluorouracil, oxaliplatin, and irinotecan (FOLFIRINOX) are valid first-line options for advanced or metastatic pancreatic cancer (mPC). However, no randomized trials comparing the two schemes have been performed. This meta-analysis aims to compare GEM-NAB and FOLFIRINOX in terms of safety and effectiveness, taking into account data from real-life studies on mPC. We systematically searched PubMed, EMBASE and Cochrane library up to November 2018 to identify retrospective or cohort studies on mPC comparing GEM-NAB and FOLFIRINOX. We included 16 retrospective studies, including 3813 patients (2123 treated with GEM-NAB and 1690 treated with FOLFIRINOX). Despite a median weighted overall survival (OS) difference in favor of FOLFIRINOX (mean difference: 1.15, 95% confidence interval CI 0.08\u207b2.22, p = 0.03), in whole population OS was similar (hazard ratio (HR = 0.99, 95% CI 0.84\u207b1.16; p = 0.9). PFS was also not different between the two arms (HR = 0.88, 95% CI 0.71\u207b1.1; p = 0.26). The overall response rate was similar (25 vs. 24% with GEM-NAB and FOLFIRINOX). Among grade 3\u207b4 toxicities, neutropenia, febrile neutropenia, and nausea were lower with GEM-NAB, while neurotoxicity and anemia were lower with FOLFIRINOX. In conclusion, despite a numerically longer median OS with FOLFIRINOX as compared to GEM-NAB, the overall risk of death and progression were similar. Their toxicity was different with less nausea, neutropenia, and febrile neutropenia with GEM-NAB, as compared to less neurotoxicity and anemia with FOLFIRINOX. Therefore, analysis of non-randomized "real world" studies to date has not provided evidence of a major benefit of one regimen over the other

Assessment of the Risk of Nodal Involvement in Rectal Neuroendocrine Neoplasms: The NOVARA Score, a Multicentre Retrospective Study

open14noRectal neuroendocrine tumors (r‐NETs) are rare tumors with overall good prognosis after complete resection. However, there is no consensus on the extension of lymphadenectomy or regarding contraindications to extensive resection. In this study, we aim to identify predictive factors that correlate with nodal metastasis in patients affected by G1–G2 r‐NETs. A retrospective analysis of G1–G2 r‐NETs patients from eight tertiary Italian centers was performed. From January 1990 to January 2020, 210 patients were considered and 199 were included in the analysis. The data for nodal status were available for 159 cases. The nodal involvement rate was 9%. A receiver operating characteristic (ROC) curve analysis was performed to identify the diameter (>11.5 mm) and Ki‐67 (3.5%), respectively, as cutoff values to predict nodal involvement. In a multivariate analysis, diameter > 11.5 mm and vascular infiltration were independently correlated with nodal involvement. A risk scoring system was constructed using these two predictive factors. Tumor size and vascular invasion are predictors of nodal involvement. In addition, tumor size > 11.5 mm is used as a driving parameter of better‐tailored treatment during pre‐operative assessment. Data from prospective studies are needed to validate these results and to guide decision‐making in r‐ NETs patients in clinical practice.openRicci A.D.; Pusceddu S.; Panzuto F.; Gelsomino F.; Massironi S.; De Angelis C.G.; Modica R.; Ricco G.; Torchio M.; Rinzivillo M.; Prinzi N.; Rizzi F.; Lamberti G.; Campana D.Ricci A.D.; Pusceddu S.; Panzuto F.; Gelsomino F.; Massironi S.; De Angelis C.G.; Modica R.; Ricco G.; Torchio M.; Rinzivillo M.; Prinzi N.; Rizzi F.; Lamberti G.; Campana D

Systemic Treatment of Patients With Gastrointestinal Cancers During the COVID-19 Outbreak: COVID-19-adapted Recommendations of the National Cancer Institute of Milan

The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak poses a major challenge in the treatment decision-making of patients with cancer, who may be at higher risk of developing a severe and deadly SARS-CoV-2 infection compared with the general population. The health care emergency is forcing the reshaping of the daily assessment between risks and benefits expected from the administration of immune-suppressive and potentially toxic treatments. To guide our clinical decisions at the National Cancer Institute of Milan (Lombardy region, the epicenter of the outbreak in Italy), we formulated Coronavirus-adapted institutional recommendations for the systemic treatment of patients with gastrointestinal cancers. Here, we describe how our daily clinical practice has changed due to the pandemic outbreak, with the aim of providing useful suggestions for physicians that are facing the same challenges worldwide

Systemic Treatment of Patients With Gastrointestinal Cancers During the COVID-19 Outbreak : COVID-19-adapted Recommendations of the National Cancer Institute of Milan

The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak poses a major challenge in the treatment decision-making of patients with cancer, who may be at higher risk of developing a severe and deadly SARS-CoV-2 infection compared with the general population. The health care emergency is forcing the reshaping of the daily assessment between risks and benefits expected from the administration of immune-suppressive and potentially toxic treatments. To guide our clinical decisions at the National Cancer Institute of Milan (Lombardy region, the epicenter of the outbreak in Italy), we formulated Coronavirus-adapted institutional recommendations for the systemic treatment of patients with gastrointestinal cancers. Here, we describe how our daily clinical practice has changed due to the pandemic outbreak, with the aim of providing useful suggestions for physicians that are facing the same challenges worldwide