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Machine learning tools are increasingly used to improve the quality of care and the soundness of a treatment plan. Explainable AI (XAI) helps users in understanding the inner mechanisms of opaque machine learning models and is a driver of trust and adoption. Explanation methods for black-box models exist, but there is a lack of user studies on the interpretability of the provided explanations. We used a Think Aloud Protocol (TAP) to explore oncologists' assessment of a lung cancer relapse prediction system with the aim of refining the purpose-built explanation model for better credibility and utility. Novel to this context, TAP is used as a neutral methodology to elicit experts' thought processes and judgements of the AI system, without explicit prompts. TAP aims to elicit the factors which influenced clinicians' perception of credibility and usefulness of the system. Ten oncologists took part in the study. We conducted a thematic analysis of their verbalized responses, generating five themes that help us to understand the context within which oncologists' may (or may not) integrate an explainable AI system into their working day., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Anjara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients., Methods: The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance., Results: The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision-recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model's predictions., Conclusion: We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients., Competing Interests: Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non-small-cell lung cancer (NSCLC)?, Materials and Methods: For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients., Results: Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients., Conclusion: Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.

Early-stage lung cancer is crucial clinically due to its insidious nature and rapid progression. Most of the prediction models designed to predict tumour recurrence in the early stage of lung cancer rely on the clinical or medical history of the patient. However, their performance could likely be improved if the input patient data contained genomic information. Unfortunately, such data is not always collected. This is the main motivation of our work, in which we have imputed and integrated specific type of genomic data with clinical data to increase the accuracy of machine learning models for prediction of relapse in early-stage, non-small cell lung cancer patients. Using a publicly available TCGA lung adenocarcinoma cohort of 501 patients, their aneuploidy scores were imputed into similar records in the Spanish Lung Cancer Group (SLCG) data, more specifically a cohort of 1348 early-stage patients. First, the tumor recurrence in those patients was predicted without the imputed aneuploidy scores. Then, the SLCG data were enriched with the aneuploidy scores imputed from TCGA. This integrative approach improved the prediction of the relapse risk, achieving area under the precision-recall curve (PR-AUC) score of 0.74, and area under the ROC (ROC-AUC) score of 0.79. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the predictions performed by the machine learning model. We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk, while also improving the predictive power by incorporating proxy genomic data not available for the actual specific patients., (©2022 AMIA - All rights reserved.)

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk., Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected., Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients' characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients., Conclusion: Overall, the reconstruction of the population's risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area., (©2021 AMIA - All rights reserved.)

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder)., Competing Interests: The authors have declared that no competing interests exist.

Background and Aims: Chronic intestinal failure is a condition causing severe impairment of intestinal functions; long-term total parenteral nutrition is required to provide adequate nutritional support., Methods: This is a 15-year follow-up study of paediatric patients with intestinal failure receiving long-term home parenteral nutrition., Results: Thirty-six patients were included in the study, all aged <16 years. Total parenteral nutrition and home parenteral nutrition were administered respectively to 100.97 and 85.20 patients-year. Today, 12 out of 36 patients are still on parenteral nutrition. A total of 99 central venous catheters were inserted, for mean 2.75 catheters/patient. The overall incidence rates of catheter-related complications was 1.79 per 1000 days-catheter for sepsis and 3.37 per 1000 days-catheter for mechanical complications. Two multivariate Cox-models have been used to examine the role of some predictors for septic or mechanical complications. The only risk factor for septic complications was the indication for parenteral nutrition, and the only predictor of mechanical complications was the insertion period., Conclusions: Our experience in the treatment of paediatric patients with gastrointestinal diseases confirms that long-term parenteral nutrition has become a safe and appropriate method in the treatment of severe chronic intestinal failure., (Copyright © 2010 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.)

Creatine transporter deficiency is an X-linked disorder characterized by mental retardation and language delay. The authors report a patient affected by creatine transport deficiency caused by a novel mutation in the SLC6A8 gene. Impairment in social interaction represents a consistent clinical finding in the few cases described to date and may be a diagnostic clue for creatine transporter deficiency in males affected by mental retardation, seizures, and language impairment.

Craniosynostosis is determined by the precocious fusion of one or more calvarial sutures leading to an abnormal skull shape. Additionally, nodular heterotopia is a disorder of neuronal migration and/or proliferation. We describe a very rare multiple congenital anomalies (MCA) syndrome in which craniosynostosis is associated with bilateral periventricular nodular heterotopia (BPNH) of the gray matter and other malformations involving hands, feet, and the gut. Clinical findings and further investigations suggest the diagnosis of craniosynostosis Fontaine-Farriaux type. To the best of our knowledge, this case is only the second report of this MCA syndrome. Based on the clinical and radiological data of the two cases reported, we hypothesize that this malformative complex may be considered a new BPNH/MCA syndrome and propose to classify it as BPNH/craniosynostosis. Previous studies demonstrated that at least two BPNH/MCA syndromes have been mapped to the Xq28 chromosomal region in which a causative gene for isolated BPNH is located. The same authors hypothesized that other BPNH syndromes could be due to microrearrangements at the same Xq28 region. Our case presents several overlapping features with some BPNH/MCA syndromes and it is possible that this new complex disorder may be caused by rearrangements at the same chromosomal region that could alter expression of different genes in Xq28., (Copyright 2001 Wiley-Liss, Inc.)