Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features

The SARS-CoV-2 virus pandemic had devastating effects on various aspects of life: clinical cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high incidence, data-driven models can support physicians in patient management. The explainability and interpretability of machine-learning models are mandatory in clinical scenarios. In this work, clinical, laboratory and radiomic features were used to train machine-learning models for COVID-19 prognosis prediction. Using Explainable AI algorithms, a multi-level explainable method was proposed taking into account the developer and the involved stakeholder (physician, and patient) perspectives. A total of 1023 radiomic features were extracted from 1589 Chest X-Ray images (CXR), combined with 38 clinical/laboratory features. After the pre-processing and selection phases, 40 CXR radiomic features and 23 clinical/laboratory features were used to train Support Vector Machine and Random Forest classifiers exploring three feature selection strategies. The combination of both radiomic, and clinical/laboratory features enabled higher performance in the resulting models. The intelligibility of the used features allowed us to validate the models' clinical findings. According to the medical literature, LDH, PaO2 and CRP were the most predictive laboratory features. Instead, ZoneEntropy and HighGrayLevelZoneEmphasis - indicative of the heterogeneity/uniformity of lung texture - were the most discriminating radiomic features. Our best predictive model, exploiting the Random Forest classifier and a signature composed of clinical, laboratory and radiomic features, achieved AUC=0.819, accuracy=0.733, specificity=0.705, and sensitivity=0.761 in the test set. The model, including a multi-level explainability, allows us to make strong clinical assumptions, confirmed by the literature insights

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings. Results: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test. Conclusions: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings

Proposal of early CT morphological criteria for response of liver metastases to systemic treatments in gastroenteropancreatic neuroendocrine tumors:Alternatives to RECIST

RECIST 1.1 criteria are commonly used with computed tomography (CT) to evaluate the efficacy of systemic treatments in patients with neuroendocrine tumors (NETs) and liver metastases (LMs), but their relevance is questioned in this setting. We aimed to explore alternative criteria using different numbers of measured LMs and thresholds of size and density variation. We retrospectively studied patients with advanced pancreatic or small intestine NETs with LMs, treated with systemic treatment in the first-and/or second-line, without early progression, in 14 European expert centers. We compared time to treatment failure (TTF) between responders and non-responders according to various criteria defined by 0%, 10%, 20% or 30% decrease in the sum of LM size, and/or by 10%, 15% or 20% decrease in LM density, measured on two, three or five LMs, on baseline (≤1 month before treatment initiation) and first revaluation (≤6 months) contrast-enhanced CT scans. Multivariable Cox proportional hazard models were performed to adjust the association between response criteria and TTF on prognostic factors. We included 129 systemic treatments (long-acting somatostatin analogs 41.9%, chemotherapy 26.4%, targeted therapies 31.8%), administered as first-line (53.5%) or second-line therapies (46.5%) in 91 patients. A decrease ≥10% in the size of three LMs was the response criterion that best predicted prolonged TTF, with significance at multivariable analysis (HR 1.90; 95% CI: 1.06–3.40; p =.03). Conversely, response defined by RECIST 1.1 did not predict prolonged TTF (p =.91), and neither did criteria based on changes in LM density. A ≥10% decrease in size of three LMs could be a more clinically relevant criterion than the current 30% threshold utilized by RECIST 1.1 for the evaluation of treatment efficacy in patients with advanced NETs. Its implementation in clinical trials is mandatory for prospective validation. Criteria based on changes in LM density were not predictive of treatment efficacy. Clinical Trial Registration: Registered at CNIL-CERB, Assistance publique hopitaux de Paris as “E-NETNET-L-E-CT” July 2018. No number was assigned. Approved by the Medical Ethics Review Board of University Medical Center Groningen.</p

Association of Upfront Peptide Receptor Radionuclide Therapy With Progression-Free Survival Among Patients With Enteropancreatic Neuroendocrine Tumors

open57noIMPORTANCE Data about the optimal timing for the initiation of peptide receptor radionuclide therapy (PRRT) for advanced, well-differentiated enteropancreatic neuroendocrine tumors are lacking. OBJECTIVE To evaluate the association of upfront PRRT vs upfront chemotherapy or targeted therapy with progression-free survival (PFS) among patients with advanced enteropancreatic neuroendocrine tumors who experienced disease progression after treatment with somatostatin analogues (SSAs). DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study analyzed the clinical records from 25 Italian oncology centers for patients aged 18 years or older who had unresectable, locally advanced or metastatic, well-differentiated, grades 1 to 3 enteropancreatic neuroendocrine tumors and received either PRRT or chemotherapy or targeted therapy after experiencing disease progression after treatment with SSAs between January 24, 2000, and July 1, 2020. Propensity score matching was done to minimize the selection bias. EXPOSURES Upfront PRRT or upfront chemotherapy or targeted therapy. MAIN OUTCOMES AND MEASURES The main outcome was the difference in PFS among patients who received upfront PRRT vs among those who received upfront chemotherapy or targeted therapy. A secondary outcome was the difference in overall survival between these groups. Hazard ratios (HRs) were fitted in a multivariable Cox proportional hazards regression model to adjust for relevant factors associated with PFS and were corrected for interaction with these factors. RESULTS Of 508 evaluated patients (mean ([SD] age, 55.7 [0.5] years; 278 [54.7%] were male), 329 (64.8%) received upfront PRRT and 179 (35.2%) received upfront chemotherapy or targeted therapy. The matched group included 222 patients (124 [55.9%] male; mean [SD] age, 56.1 [0.8] years), with 111 in each treatment group. Median PFS was longer in the PRRT group than in the chemotherapy or targeted therapy group in the unmatched (2.5 years [95%CI, 2.3-3.0 years] vs 0.7 years [95%CI, 0.5-1.0 years]; HR, 0.35 [95%CI, 0.28-0.44; P &lt; .001]) and matched (2.2 years [95% CI, 1.8-2.8 years] vs 0.6 years [95%CI, 0.4-1.0 years]; HR, 0.37 [95%CI, 0.27-0.51; P &lt; .001]) populations. No significant differences were shown in median overall survival between the PRRT and chemotherapy or targeted therapy groups in the unmatched (12.0 years [95%CI, 10.7-14.1 years] vs 11.6 years [95%CI, 9.1-13.4 years]; HR, 0.81 [95%CI, 0.62-1.06; P = .11]) and matched (12.2 years [95% CI, 9.1-14.2 years] vs 11.5 years [95%CI, 9.2-17.9 years]; HR, 0.83 [95%CI, 0.56-1.24; P = .36]) populations. The use of upfront PRRT was independently associated with improved PFS (HR, 0.37; 95%CI, 0.26-0.51; P &lt; .001) in multivariable analysis. After adjustment of values for interaction, upfront PRRT was associated with longer PFS regardless of tumor functional status (functioning: adjusted HR [aHR], 0.39 [95%CI, 0.27-0.57]; nonfunctioning: aHR, 0.29 [95%CI, 0.16-0.56]), grade of 1 to 2 (grade 1: aHR, 0.21 [95%CI, 0.12-0.34]; grade 2: aHR, 0.52 [95%CI, 0.29-0.73]), and site of tumor origin (pancreatic: aHR, 0.41 [95%CI, 0.24-0.61]; intestinal: aHR, 0.19 [95%CI, 0.11-0.43]) (P &lt; .001 for all). Conversely, the advantage was not retained in grade 3 tumors (aHR, 0.31; 95%CI, 0.12-1.37; P = .13) or in tumors with a Ki-67 proliferation index greater than 10% (aHR, 0.73; 95%CI, 0.29-1.43; P = .31). CONCLUSIONS AND RELEVANCE In this cohort study, treatment with upfront PRRT in patients with enteropancreatic neuroendocrine tumors who had experienced disease progression with SSA treatment was associated with significantly improved survival outcomes compared with upfront chemotherapy or targeted therapy. Further research is needed to investigate the correct strategy, timing, and optimal specific sequence of these therapeutic options.openPusceddu, Sara; Prinzi, Natalie; Tafuto, Salvatore; Ibrahim, Toni; Filice, Angelina; Brizzi, Maria Pia; Panzuto, Francesco; Baldari, Sergio; Grana, Chiara M.; Campana, Davide; Davì, Maria Vittoria; Giuffrida, Dario; Zatelli, Maria Chiara; Partelli, Stefano; Razzore, Paola; Marconcini, Riccardo; Massironi, Sara; Gelsomino, Fabio; Faggiano, Antongiulio; Giannetta, Elisa; Bajetta, Emilio; Grimaldi, Franco; Cives, Mauro; Cirillo, Fernando; Perfetti, Vittorio; Corti, Francesca; Ricci, Claudio; Giacomelli, Luca; Porcu, Luca; Di Maio, Massimo; Seregni, Ettore; Maccauro, Marco; Lastoria, Secondo; Bongiovanni, Alberto; Versari, Annibale; Persano, Irene; Rinzivillo, Maria; Pignata, Salvatore Antonio; Rocca, Paola Anna; Lamberti, Giuseppe; Cingarlini, Sara; Puliafito, Ivana; Ambrosio, Maria Rosaria; Zanata, Isabella; Bracigliano, Alessandra; Severi, Stefano; Spada, Francesca; Andreasi, Valentina; Modica, Roberta; Scalorbi, Federica; Milione, Massimo; Sabella, Giovanna; Coppa, Jorgelina; Casadei, Riccardo; Di Bartolomeo, Maria; Falconi, Massimo; de Braud, FilippoPusceddu, Sara; Prinzi, Natalie; Tafuto, Salvatore; Ibrahim, Toni; Filice, Angelina; Brizzi, Maria Pia; Panzuto, Francesco; Baldari, Sergio; Grana, Chiara M.; Campana, Davide; Davì, Maria Vittoria; Giuffrida, Dario; Zatelli, Maria Chiara; Partelli, Stefano; Razzore, Paola; Marconcini, Riccardo; Massironi, Sara; Gelsomino, Fabio; Faggiano, Antongiulio; Giannetta, Elisa; Bajetta, Emilio; Grimaldi, Franco; Cives, Mauro; Cirillo, Fernando; Perfetti, Vittorio; Corti, Francesca; Ricci, Claudio; Giacomelli, Luca; Porcu, Luca; Di Maio, Massimo; Seregni, Ettore; Maccauro, Marco; Lastoria, Secondo; Bongiovanni, Alberto; Versari, Annibale; Persano, Irene; Rinzivillo, Maria; Pignata, Salvatore Antonio; Rocca, Paola Anna; Lamberti, Giuseppe; Cingarlini, Sara; Puliafito, Ivana; Ambrosio, Maria Rosaria; Zanata, Isabella; Bracigliano, Alessandra; Severi, Stefano; Spada, Francesca; Andreasi, Valentina; Modica, Roberta; Scalorbi, Federica; Milione, Massimo; Sabella, Giovanna; Coppa, Jorgelina; Casadei, Riccardo; Di Bartolomeo, Maria; Falconi, Massimo; de Braud, Filipp

Shallow and deep learning classifiers in medical image analysis.

An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians' decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between "shallow" learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and "deep" learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence.Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice. Key points • Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics).• Deep classifiers implement automatic feature extraction and classification.• The classifier selection is based on data and computational resources availability, task, and explanation needs

Shallow and deep learning classifiers in medical image analysis

Abstract An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians’ decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between “shallow” learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and “deep” learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence. Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice. Key points • Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics). • Deep classifiers implement automatic feature extraction and classification. • The classifier selection is based on data and computational resources availability, task, and explanation needs. Graphical Abstrac

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

Acknowledgements: The authors would like to thank Marco Insalaco and Noemi Campagna for their substantial contribution to the dataset preparation phase.Funder: Università degli Studi di PalermoBreast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer

Fault rocks within the blueschist metabasalts of the Diamante-Terranova unit (southern Italy): potential fossil record of intermediate-depth subduction earthquakes

We report the first evidence of fault rocks developed during high-pressure/low temperature subduction-related metamorphism, within quartz+epidote pods embedded in the glaucophane-lawsonite-bearing ophiolitic metabasalts of the Diamante-Terranova unit (Calabria, Italy). Fault rocks occur as relic injections appearing as thin dark seams, locally showing an internal foliation characterized by tabular, curvilinear and meander-like shapes, and consist of very fine grains of glaucophane and titanite, locally including survivor clasts of epidote and lawsonite. Some boudinaged veins show glaucophane fibres in the boudin necks, marking a clear HP/LT syn-metamorphic origin at ca. 30 km depth. The injected fault rocks can be alternatively interpreted either as pseudotachylytes or as fluidized ultracataclasites. Although subsequent recrystallization largely obliterated primary diagnostic features, the occurrence of (i) different coloured flow streaks, characterized by alternating layers of glaucophane and titanite, (ii) well-developed flow-folds and (iii) corroded epidote survivor crystals, could indicate a viscous flow of molten material characterized by a non-uniform chemical composition. With this in mind, we support the hypothesis that these fine-grained veins were originally pseudotachylytes generated by the frictional melting of the glaucophane-rich layers of the Diamante-Terranova metabasalts, likely related with seismic events occurring during the Eocene along thrust faults within the subducting oceanic Ligurian lithosphere. The lack of evidence for pseudotachylyte relics in the metabasalt source rock argues for a selective preservation, largely dependent on the efficient mechanical shielding action of the stiffer quartz+epidote pods

Structural and Stratigraphic Setting of Campagna and Giffoni Tectonic Windows: New Insights on the Orogenic Evolution of the Southern Apennines (Italy)

We present a structural study on the tectonic windows of Gi oni and Campagna, located in the western sector of the southern Apennines (Italy).We analyzed thrusts, folds, and related minor deformation structures. Here, a major in-sequence E-verging thrust fault juxtaposes Meso-Cenozoic successions of the Apennine Platform (Picentini Mts unit) and the Lagonegro-Molise Basin (Frigento unit). However, out-of-sequence thrusts duplicated the tectonic pile with the interposition of the upper Miocene wedge-top basin deposits of the Castelvetere Group. We reconstructed the orogenic evolution of these two tectonic windows, including five deformation phases. The first (D1) was related to the in-sequence thrusting with minor thrusts and folds, widespread both in the footwall and the hanging wall. A subsequent extension (D2) has formed normal faults crosscutting the D1 thrusts and folds. All structures were subsequently a ected by two shortening stages (D3 and D4), which also deformed the upper Miocene wedge top basin deposits of the Castelvetere Group. We interpreted the D3–D4 structures as related to an out-of-sequence thrust system defined by a main frontal E-verging thrust and lateral ramps characterized by N and S vergences. Low-angle normal faults were formed in the hanging wall of the major thrusts. Out-of-sequence thrusts are observed in the whole southern Apennines, recording a crustal shortening event that occurred in the late Messinian–early Pliocene. Finally, we suggest that the two tectonic windows are the result of the formation of an E–W trending regional antiform, associated with a late S-verging back-thrust, that has been eroded and crosscut by normal faults (D5) in the Early Pleistocene