Equilibrium Asset Pricing with Time-Varying Pessimism

capital asset pricing;general equilibrium;uncertainty;financial risk;model misspecification;Knightian uncertainty;first order risk aversion

A tool for radiotherapy plan evaluation analysis: generalise Uniform Ideal Dose (gUIDE)

In radiotherapy, treatment planning is the process in which the appropriate dose distribution is planned for a specific patient. However, there is no consensus on what the ‘optimal’ plan should be and on how to measure plan quality. The purpose of this study was to develop a tool called a ‘generalized Uniform Ideal Dose’ (gUIDE) that produces an ‘ideal’ dose distribution based on single patient anatomy and dose prescription. By comparing the clinical achieved dose distribution with gUIDE a quantitative measure of plan quality can be derived. gUIDE is based on an exponential function of dose fall-off outside the tumor volume. The algorithm does not require any specification of the treatment machine but only patient geometry information. gUIDE fall-off parameter was properly derived in a simple geometry dose profile. Overall, gUIDE showed a lower DVH than the DVH generated using the clinical treatment planning system, as it was expected for a baseline ideal condition. In the clinical validation, although the statistical test showed significant differences between the two groups, overall values were similar for all structures between gUIDE and PlanIQ. A baseline dose gUIDE was implemented, optimised and evaluated. gUIDE could be accurate enough to be used as baseline to help in the plan evaluation process

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

Molecular analysis of PDGFRA and PDGFRB genes by rapid single-strand conformation polymorfism (SSCP) in patients with core-binding factor leukaemias with KIT or FLT3 mutation

BACKGROUND: Mutations involving KIT and FLT3 genes, encoding tyrosine kinase (TK) membrane receptors, are detected in core-binding factor leukaemia (CBFL) patients. PDFGRA and PDGFRB encode class III TK receptors and are involved both in physiological processes and in the pathogenesis of haematological and solid tumours. The aim of this study was to investigate if PDGFR mutations are involved in CBFL. PATIENTS AND METHODS: In order to detect PDGFR mutations in CBFL, 35 patients without KIT or FLT3 mutations patients were screened by rapid and sensitive single-strand conformation polymorphism (SSCP) analysis. Sequence analysis was performed in polymerase chain reaction (PCR) products showing altered mobility in SSCP analysis in order to determine the nucleotide changes. RESULTS: Three types of single-nucleotide polymorphism (SNP) were detected in the PDGFRA gene (exon 12, exon 13 and exon 18) while no mutation of PDGFRB was detected in the tested CBFLs. CONCLUSION: These data showed that no pathogenic mutations in PDGFRA and PDGFRB were detected in the context of CBFL without KIT and FLT3 mutations. Thus, PDGFR genes do not seem to be involved in CBFL and future studies are needed to establish the genetic causes of the disease in these particular patients

Using MRI Texture Analysis Machine Learning Models to Assess Graft Interstitial Fibrosis and Tubular Atrophy in Patients with Transplanted Kidneys

Objective: Interstitial fibrosis/tubular atrophy (IFTA) is a common, irreversible, and progressive form of chronic kidney allograft injury, and it is considered a critical predictor of kidney allograft outcomes. The extent of IFTA is estimated through a graft biopsy, while a non-invasive test is lacking. The aim of this study was to evaluate the feasibility and accuracy of an MRI radiomic-based machine learning (ML) algorithm to estimate the degree of IFTA in a cohort of transplanted patients. Approach: Patients who underwent MRI and renal biopsy within a 6-month interval from 1 January 2012 to 1 March 2021 were included. Stable MRI sequences were selected, and renal parenchyma, renal cortex and medulla were segmented. After image filtering and pre-processing, we computed radiomic features that were subsequently selected through a LASSO algorithm for their highest correlation with the outcome and lowest intercorrelation. Selected features and relevant patients' clinical data were used to produce ML algorithms using 70% of the study cases for feature selection, model training and validation with a 10-fold cross-validation, and 30% for model testing. Performances were evaluated using AUC with 95% confidence interval. Main results: A total of 70 coupled tests (63 patients, 35.4% females, mean age 52.2 years) were included and subdivided into a wider cohort of 50 for training and a smaller cohort of 20 for testing. For IFTA >= 25%, the AUCs in test cohort were 0.60, 0.59, and 0.54 for radiomic features only, clinical variables only, and a combined radiomic-clinical model, respectively. For IFTA >= 50%, the AUCs in training cohort were 0.89, 0.84, and 0.96, and in the test cohort, they were 0.82, 0.83, and 0.86, for radiomic features only, clinical variables only, and the combined radiomic-clinical model, respectively. Significance: An ML-based MRI radiomic algorithm showed promising discrimination capacity for IFTA > 50%, especially when combined with clinical variables. These results need to be confirmed in larger cohorts

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