Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results

We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence of a feature in one part of the image affects the appearance of another feature in a different part of the image. Our method consists of a convolutional neural network backbone and a causality-factors extractor module, which computes weights to enhance each feature map according to its causal influence in the scene. We develop different architecture variants and empirically evaluate all the models on two public datasets of prostate MRI images and breast histopathology slides for cancer diagnosis. We study the effectiveness of our module both in fully-supervised and few-shot learning, we assess its addition to existing attention-based solutions, we conduct ablation studies, and investigate the explainability of our models via class activation maps. Our findings show that our lightweight block extracts meaningful information and improves the overall classification, together with producing more robust predictions that focus on relevant parts of the image. That is crucial in medical imaging, where accurate and reliable classifications are essential for effective diagnosis and treatment planning.Comment: Added experiments in which we integrate our Mulcat module to existing models using Bottleneck Attention Modules, and added experiments in Few-Shot Learning; 19 page

Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.Comment: 9 pages, 2 figures, accepted on Aug 07 2023 for ICCV-CVAMD 2023 and to be published in the proceeding

High resolution CT and histological findings in idiopathic pleuroparenchymal fibroelastosis: Features and differential diagnosis

Idiopathic pleuroparenchymal fibroelastosis (IPPFE) is a recently described clinical-pathologic entity characterized by pleural and subpleural parenchymal fibrosis, mainly in the upper lobes. As this disease is extremely rare (only 7 cases have been described in the literature to date) poorly defined cases of IPPFE can go unrecognized

From "traction bronchiectasis" to honeycombing in idiopathic pulmonary fibrosis: A spectrum of bronchiolar remodeling also in radiology?

The diagnostic and prognostic impact of traction bronchiectasis on high resolution CT scan (HRCT) in patients suspected to have idiopathic pulmonary fibrosis (IPF) is increasing significantly

The Association Between Intolerance of Uncertainty, Emotion Dysregulation, and Anxiety in Italian Non-Clinical Pre-Adolescents and Adolescents

Background: Anxiety symptoms are rather frequent in adolescence and associated with long-term negative consequences. Therefore, expanding knowledge on the factors that may underlie anxiety symptomatology is extremely relevant; to this end, intolerance of uncertainty and emotion dysregulation are of key interest. This study aimed to deepen the relation between intolerance of uncertainty and emotion regulation difficulties and to explore the role of these constructs in explaining anxiety levels in adolescence. Methods: Three hundred and fifty Italian non-clinical pre-adolescents and adolescents (age range: 11-17, 53.4% boys) entered the study between November 2021 and March 2022. We administered an online survey containing the Difficulties in Emotion Regulation Scale, Intolerance of Uncertainty Scale-Revised, Self-Administered Psychiatric Scales for Children and Adolescents-Anxiety scale, and Depression Anxiety Stress Scales-21. Pearson’s correlations were calculated to examine the relation between intolerance of uncertainty, emotion dysregulation dimensions, and different anxiety symptoms. A hierarchical linear regression was performed to test the predictive role of intolerance of uncertainty and specific emotion regulation strategies on generalized anxiety symptoms. Results: All emotion dysregulation dimensions, except Awareness, were significantly correlated with intolerance of uncertainty and the different anxiety manifestations. Intolerance of uncertainty was associated with all anxiety symptoms, but to a greater extent with generalized and school-related anxiety. Finally, both intolerance of uncertainty and specific emotion dysregulation dimensions (i.e., Goals and Strategies) predicted generalized anxiety symptoms; however, the emotion dysregulation block led to a higher increase in explained variance than intolerance of uncertainty did. Conclusion: Intolerance of uncertainty, emotion dysregulation, and anxiety symptoms emerged to be strictly associated. Moreover, the contribution of both intolerance of uncertainty and specific emotion regulation difficulties to the putative development of generalized anxiety in adolescence has been tentatively supported. Particularly, emotion dysregulation seems to play a more relevant role in generalized anxiety compared to intolerance of uncertainty

Detection and Investigation of Extracellular Vesicles in Serum and Urine Supernatant of Prostate Cancer Patients

none13no: Prostate Cancer (PCa) is one of the most frequently identified urological cancers. PCa patients are often over-diagnosed due to still not highly specific diagnostic methods. The need for more accurate diagnostic tools to prevent overestimated diagnosis and unnecessary treatment of patients with non-malignant conditions is clear, and new markers and methods are strongly desirable. Extracellular vesicles (EVs) hold great promises as liquid biopsy-based markers. Despite the biological and technical issues present in their detection and study, these particles can be found highly abundantly in the biofluid and encompass a wealth of macromolecules that have been reported to be related to many physiological and pathological processes, including cancer onset, metastasis spreading, and treatment resistance. The present study aims to perform a technical feasibility study to develop a new workflow for investigating EVs from several biological sources. Serum and urinary supernatant EVs of PCa, benign prostatic hyperplasia (BPH) patients, and healthy donors were isolated and investigated by a fast, easily performable, and cost-effective cytofluorimetric approach for a multiplex detection of 37 EV-antigens. We also observed significant alterations in serum and urinary supernatant EVs potentially related to BPH and PCa, suggesting a potential clinical application of this workflow.openSalvi, Samanta; Bandini, Erika; Carloni, Silvia; Casadio, Valentina; Battistelli, Michela; Salucci, Sara; Erani, Ilaria; Scarpi, Emanuela; Gunelli, Roberta; Cicchetti, Giacomo; Guescini, Michele; Bonafè, Massimiliano; Fabbri, FrancescoSalvi, Samanta; Bandini, Erika; Carloni, Silvia; Casadio, Valentina; Battistelli, Michela; Salucci, Sara; Erani, Ilaria; Scarpi, Emanuela; Gunelli, Roberta; Cicchetti, Giacomo; Guescini, Michele; Bonafè, Massimiliano; Fabbri, Francesc

Stability Program in Dendritic Cell Vaccines: A “Real-World” Experience in the Immuno-Gene Therapy Factory of Romagna Cancer Center

Advanced therapy medical products (ATMPs) are rapidly growing as innovative medicines for the treatment of several diseases. Hence, the role of quality analytical tests to ensure consistent product safety and quality has become highly relevant. Several clinical trials involving dendritic cell (DC)-based vaccines for cancer treatment are ongoing at our institute. The DC-based vaccine is prepared via CD14+ monocyte differentiation. A fresh dose of 10 million DCs is administered to the patient, while the remaining DCs are aliquoted, frozen, and stored in nitrogen vapor for subsequent treatment doses. To evaluate the maintenance of quality parameters and to establish a shelf life of frozen vaccine aliquots, a stability program was developed. Several parameters of the DC final product at 0, 6, 12, 18, and 24 months were evaluated. Our results reveal that after 24 months of storage in nitrogen vapor, the cell viability is in a range between 82% and 99%, the expression of maturation markers remains inside the criteria for batch release, the sterility tests are compliant, and the cell costimulatory capacity unchanged. Thus, the data collected demonstrate that freezing and thawing do not perturb the DC vaccine product maintaining over time its functional and quality characteristics

Expansion history and f(R) modified gravity

We attempt to fit cosmological data using $f(R)$ modified Lagrangians containing inverse powers of the Ricci scalar varied with respect to the metric. While we can fit the supernova data well, we confirm the $a\propto t^{1/2}$ behaviour at medium to high redshifts reported elsewhere and argue that the easiest way to show that this class of models are inconsistent with the data is by considering the thickness of the last scattering surface. For the best fit parameters to the supernova data, the simplest 1/R model gives rise to a last scattering surface of thickness $\Delta z\sim 530$, inconsistent with observations.Comment: accepted in JCAP, presentation clarified, results and conclusions unchange