Giovanni Paolo Research Articles

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

BackgroundRisk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy.MethodsWe made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori “Giovanni Paolo II” in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC).ResultsThe TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively.ConclusionsOur approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach.

About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features. First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.

La reciproca collaborazione a servizio della persona umana tra la Chiesa cattolica e gli Stati dopo il Concilio Ecumenico Vaticano II e gli accordi di Villa Madama

Mutual Collaboration in the Service of the Human Person between the Catholic Church and States after the Second Vatican Ecumenical Council and the Villa Madama Agreements
 ABSTRACT: The article examines the ways of expressing the sana cooperatio between the Holy See and the political community in the aftermath of the Second Vatican Council and the Villa Madama Accords. It will focus on the its characteristic traits as emerging from the pontificates of John Paul II, Benedict XVI and Francis and then examine the instruments of cooperatio, in particular the role of the Secretariat of State, the papal nuncios and the concordats.
 SOMMARIO: 1. Premessa - 2. Chiesa e comunità politica all’epoca dei tre papi “stranieri” - 2.1 Giovanni Paolo II - 2.2 Benedetto XVI - 2.3 Francesco - 3. Gli strumenti della sana cooperatio.

Clipping Effect on the Grain Nitrogen and Protein Fractions of Ancient and Old Wheats Grown in a Mediterranean Environment

This study is the first to assess the effects of clipping, cultivar, season, and their interactions on the protein composition of six old and ancient wheat cultivars (n = 6). For this, nitrogen content, the proportion of wheat protein fractions, and the molecular weight distribution of the extractable and unextractable glutenin polymers were investigated as a function of cultivar and clipping in two consecutive seasons. The relationships between genotypic variation in grain nitrogen and protein fraction content under clipping and non-clipping conditions were also assessed. Clipping delayed and shortened the grain filling period of all of the cultivars. The protein composition of some cultivars behaved differently to clipping due to differences in the environmental conditions of S1 (exceptional dry season) and S2 (rainy season). In S1, clipping decreased the ratio of gliadins over glutenins (GLI/GLU) (<1) of Cappelli and Giovanni Paolo, while in S2, clipping improved the GLI/GLU of Giovanni Paolo, Monlis, and Norberto. The unextractable polymeric proteins were not affected by clipping. Khorasan was shown to be indifferent to clipping in S1 and S2. These results suggest that it is possible to have ancient/old wheats suitable for a dual-purpose system, in different climatic conditions, while maintaining good grain quality traits. The increased market demand for ancient and old wheats presents an economic opportunity for farmers who adopt the dual-purpose technique to cultivate these resilient crops again and increase their profit margins and revenues.

Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori “Giovanni Paolo II”. Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.

Assessing the cost-effectiveness of waiting list reduction strategies for a breast radiology department: a real-life case study

BackgroundA timely diagnosis is essential for improving breast cancer patients’ survival and designing targeted therapeutic plans. For this purpose, the screening timing, as well as the related waiting lists, are decisive. Nonetheless, even in economically advanced countries, breast cancer radiology centres fail in providing effective screening programs. Actually, a careful hospital governance should encourage waiting lists reduction programs, not only for improving patients care, but also for minimizing costs associated with the treatment of advanced cancers. Thus, in this work, we proposed a model to evaluate several scenarios for an optimal distribution of the resources invested in a Department of Breast Radiodiagnosis.Materials and methodsParticularly, we performed a cost-benefit analysis as a technology assessment method to estimate both costs and health effects of the screening program, to maximise both benefits related to the quality of care and resources employed by the Department of Breast Radiodiagnosis of Istituto Tumori “Giovanni Paolo II” of Bari in 2019. Specifically, we determined the Quality-Adjusted Life Year (QALY) for estimating health outcomes, in terms of usefulness of two hypothetical screening strategies with respect to the current one. While the first hypothetical strategy adds one team made up of a doctor, a technician and a nurse, along with an ultrasound and a mammograph, the second one adds two afternoon teams.ResultsThis study showed that the most cost-effective incremental ratio could be achieved by reducing current waiting lists from 32 to 16 months. Finally, our analysis revealed that this strategy would also allow to include more people in the screening programs (60,000 patients in 3 years).

Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori “Giovanni Paolo II” of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute’s Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.

The Discourse on Perspective in Giovanni Paolo Lomazzo’s Trattato dell’Arte della Pittura

The paper presents the complex discourse on perspective which the Milanese painter Giovanni Paolo Lomazzo writes in book V of his Trattato dell’Arte della Pittura (1584), explaining why the theoretical and practical discourse on the subject can be interpreted as the result of the artist’s erudition, personal connections and direct artistic experience.

A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants.

A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants.

The role of unpaired image-to-image translation for stain color normalization in colorectal cancer histology classification

BackgroundHistological assessment of colorectal cancer (CRC) tissue is a crucial and demanding task for pathologists. Unfortunately, manual annotation by trained specialists is a burdensome operation, which suffers from problems like intra- and inter-pathologist variability. Computational models are revolutionizing the Digital Pathology field, offering reliable and fast approaches for challenges like tissue segmentation and classification. With this respect, an important obstacle to overcome consists in stain color variations among different laboratories, which can decrease the performance of classifiers. In this work, we investigated the role of Unpaired Image-to-Image Translation (UI2IT) models for stain color normalization in CRC histology and compared to classical normalization techniques for Hematoxylin-Eosin (H&E) images. MethodsFive Deep Learning normalization models based on Generative Adversarial Networks (GANs) belonging to the UI2IT paradigm have been thoroughly compared to realize a robust stain color normalization pipeline. To avoid the need for training a style transfer GAN between each pair of data domains, in this paper we introduce the concept of training by exploiting a meta-domain, which contains data coming from a wide variety of laboratories. The proposed framework enables a huge saving in terms of training time, by allowing to train a single image normalization model for a target laboratory. To prove the applicability of the proposed workflow in the clinical practice, we conceived a novel perceptive quality measure, which we defined as Pathologist Perceptive Quality (PPQ). The second stage involved the classification of tissue types in CRC histology, where deep features extracted from Convolutional Neural Networks have been exploited to realize a Computer-Aided Diagnosis system based on a Support Vector Machine (SVM). To prove the reliability of the system on new data, an external validation set composed of N = 15,857 tiles has been collected at IRCCS Istituto Tumori “Giovanni Paolo II”. ResultsThe exploitation of a meta-domain consented to train normalization models that allowed achieving better classification results than normalization models explicitly trained on the source domain. PPQ metric has been found correlated to quality of distributions (Fréchet Inception Distance — FID) and to similarity of the transformed image to the original one (Learned Perceptual Image Patch Similarity — LPIPS), thus showing that GAN quality measures introduced in natural image processing tasks can be linked to pathologist evaluation of H&E images. Furthermore, FID has been found correlated to accuracies of the downstream classifiers. The SVM trained on DenseNet201 features allowed to obtain the highest classification results in all configurations. The normalization method based on the fast variant of CUT (Contrastive Unpaired Translation), FastCUT, trained with the meta-domain paradigm, allowed to achieve the best classification result for the downstream task and, correspondingly, showed the highest FID on the classification dataset. ConclusionsStain color normalization is a difficult but fundamental problem in the histopathological setting. Several measures should be considered for properly assessing normalization methods, so that they can be introduced in the clinical practice. UI2IT frameworks offer a powerful and effective way to perform the normalization process, providing realistic images with proper colorization, unlike traditional normalization methods that introduce color artifacts. By adopting the proposed meta-domain framework, the training time can be reduced, and the accuracy of downstream classifiers can be increased.

Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability.

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori "Giovanni Paolo II" and made publicly available to ease research concerning the quantification of tumor cellularity.

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.

Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Thus, our framework aims at shortening the distance between AI and clinical practice.

The Fine Legacy of Giovanni P. Martelli (1935–2020), a Preeminent Plant Virologist and the Founder of Modern Grapevine Virology

Giovanni Paolo Martelli passed away on 8 January 2020 [...].

Motivated by Hatred: Defoe, Deism, and the Novel

Motivated by Hatred: Defoe, Deism, and the Novel

A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I–III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori ‘Giovanni Paolo II’ in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.

Validation of Argo (Automatic record generator for Onco-Hematology), a New App Supporting the Automatic Conversion of Paper-Based Pathology Reports in Standardized Ecrfs

Background and aims. The scarce accessibility to integrated systems limits the advantage of using real-world medical data for translational research purposes. ARGO (Automatic Report Generation for Onco-hematology) converts paper-based pathology reports in electronic Case Report Forms (eCRFs) exploiting Optical Character Recognition and Natural Language Processing technologies [Zaccaria et al., Sci. Rep., 2021]. Here, we present for the first time the App version of ARGO, designed to support physicians and data entries in rapidly generating eCRFs in a standardized and filtrable fashion. For scalability purposes, we tested ARGO App by processing n. 501 pathology reports from eight Italian Centers, including Hodgkin (HL), Diffuse Large B-Cell (DLBCL), Follicular (FCL), Mantle Cell (MCL), and T-Cell (TCL) lymphoma diagnoses. Methods. Validation involved six expert hematologists who generated eCRFs by simply acquiring photographs of each paper-based report using commercially available camera-equipped smartphones (Apple® iPhones, IOS version 15). The set included n. 347 and n. 154 reports from IRCCS Istituto Tumori 'Giovanni Paolo II’ (internal series, IS) and seven Italian cooperative Centers (external series, ES), respectively. Overall, they comprised n. 139 HL, n. 154 DLBCL, n. 109 FL, n. 76 MCL, n. 6 TCL, n. 17 unclassified describing major immunohistochemistry markers (IMs) as MYC, BCL2, BCL6, CD10, CD20, Cyclin D1, CD79a, CD15, CD30, PAX5, CD5, CD3, and Ki-67 proliferation index. The automatic process of diagnosis assignment by the ARGO algorithm (developed in Python) was imposed to depend on the highest matching rate between the detected IMs and corresponding classification as from the National Institute of Health in accordance with the International Classification of Diseases, 10th (ICD-10) and oncology (ICD-O) versions. To overcome potential misdiagnosis risk, a Random Forest (RF) model was trained on the IMs set of the IS, tested on the ES, and combined with ARGO algorithm. The performance of the App was assessed for accuracy and F1-score, which is a more sensitive metric. Results. The ARGO App includes two use-cases at prospectically acquiring and retrospectively reading reports by users (physicians and/or data-managers). The first use-case allows users to acquire pathology reports via mobile phone's camera. The second use-case leads to search patients’ data filtering by "Report ID", "Name", "Surname” and "Type of diagnosis". For each new record, ARGO converts information about patients’ demography, diagnosis, tissue of origin of samples (lymph-node, extra-nodal, bone marrow, and peripheral blood), and IMs expressions. ARGO successfully converted 490 (97.8%) reports into structured eCRFs (overall, n. 18,816 data). In terms of accuracy (Fig. 1A), MYC, Cyclin D1, CD79a, CD15, EMA, BCL2 (by fluorescent in situ hybridization), IgD, IgM, EBV, Ki-67 detection achieved among 85.7% and 99.4% in both series. BCL2, BCL6, CD10, CD20, CD30, PAX5, CD23, CD5, and CD45/LCA achieved among 65.1% and 85.6% for the IS and among 71.0% and 90.9% for the ES. MUM1 and CD3 achieved 56.2% and 71.4%, and 72.0% and 79.9% for IS and ES, respectively. Concerning the F1-score (Fig. 1B), although no significant differences were observed between the two series, on average, biomarkers gave a score that was lower of 20.2% for IS and 35.3% for ES compared to accuracy. Interestingly, Ki-67 proliferation index, MYC, CD10, CD20, Cyclin D1, CD79a, CD15, CD23, and CD5 achieved among 73.5% and 85.9% for the IS and among 72.4% and 85.5% for the ES. The capturing of diagnosis achieved 87.3% and 82.5% of accuracy, and 87.3% and 83.0% of F1-score for IS and ES, respectively. Focusing on individual diagnoses in the ES, HL, MCL, DLBCL, FCL, and TCL reached 90.0%, 88.5%, 84.9%, 76.5%, 33.3%, respectively. Of these n. 154 reports, n. 52 (34%) were detected by the sole ARGO algorithm, n. 28 (18%) by RF, n. 67 (43%) by a combination of both, while n. 7 (5%) remained unclassified. Conclusions. We validated ARGO App that robustly converts paper-based pathology reports of major lymphoma subtypes into structured eCRFs. ARGO is feasible and easily transferable into the daily practice to generate standardized patients’ clinical records for clinical and translational research purposes. Ongoing efforts are aiming at enlarging the TCL cohort of pathology reports and developing a multilanguage version for other languages than Italian. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Suitability of Improved and Ancient Italian Wheat for Bread-Making: A Holistic Approach.

Ancient and old wheat grains are gaining interest as a genetic reservoir to develop improved Italian genotypes with peculiar features. In this light, the aim of this study was to assess the baking performance of two improved einkorn (Monlis and Norberto) and two improved emmer (Padre Pio and Giovanni Paolo) genotypes in comparison with two Italian landraces (Garfagnana and Cappelli) and Khorasan. This set was evaluated following a holistic approach considering the flour, dough, and bread properties. The results showed that the flour properties, dough rheology, pasting, and fermentation parameters, as well as the bread properties, significantly differed among the studied genotypes. Cappelli produced the bread with the best quality, i.e., the highest volume and lowest firmness. Despite having the same pedigrees, Giovanni Paolo and Padre Pio resulted in significantly different technological properties. Giovanni Paolo flour showed the highest protein content and provided a dough with a high gas production capacity, resulting in the bread having a similar firmness to Cappelli. Padre Pio flour provided bread having a similar volume to Cappelli but a high firmness similar to Khorasan and Garfagnana. The einkorn genotypes, Monlis and Norberto, showed poor fermentation properties and high gelatinization viscosity that resulted in bread with poor quality. Alternatively, they could be more suitable for making non-fermented flatbreads. Our results showed that the improved wheat showed a high versatility of features, which offers bakers a flexible material to make a genotype of bread types.

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.

Remote vs in-person BRCA1/2 non-carriers test disclosure: patients' choice during Covid-19 pandemic restriction.

During Covid-19 pandemic most hospitals have restricted in-person delivery of non-essential healthcare services, including genetic testing delivery, to slow the spread of the virus. Our Onco-Genetic Service also faced this challenging period and had to re-organize its clinical practice with the use of tele-health. Aim of the present paper is to understand whether and how Covid-19-related changes in medical practice influenced patients’ satisfaction about the health service provided. 125 BRCA1/2 non carriers (109/125, 87.2% female and 16/125, 12.8% male) in Istituto Tumori “Giovanni Paolo II” of Bari were enrolled. All participants were asked to choose whether they prefer in-person or remote post-test counselling session. Basing on patients’ choice, two groups of subjects were composed. One week after the post-test counselling session, participants were phone called and asked to complete: a socio-demographic form, a brief structured interview about their Covid-19 related worries and their satisfaction with the health service provided, Hospital Anxiety and Depression Scale and Fear of Covid-19 scale. Qualitative information about patients’ choice were also collected. No significant difference about patients’ satisfaction with the health service provided emerged between groups. Patients who preferred remote post-test counselling had higher anxiety, worries and fear-of Covid-19 than the others. All remote-counselling subjects preferred tele-genetics because of Covid-19 security, would choose it again and would recommend it to others. Cancer tele-genetics offers good guarantees of comfort and efficacy, but patients’ choices are related to personal and psychological variables. The use of tele-genetics has to be a patient’s choice.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10689-022-00307-y.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.