Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.
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ABSTRACT:
SUBMITTER: Muller L
PROVIDER: S-EPMC8150393 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Intrahepatic cholangiocarcinoma (ICC) remains a highly heterogeneous disease with poor prognosis. Tumor-infiltrating lymphocytes were predictive in various cancers, but their prognostic value in ICC is less clear. A total of 168 ICC patients who had received liver resection were enrolled and assigned to the derivation cohort. Sixteen immune markers in tumor and peritumor regions were examined by immunohistochemistry. A least absolute shrinkage and selection operator model was used to identify prognostic markers and to establish an immune signature for ICC (ISICC ). An ISICC -applied prediction model was built and validated in another independent dataset. Five immune features, including CD3peritumor (P) , CD57P , CD45RAP , CD66bintratumoral (T) and PD-L1P , were identified and integrated into an individualized ISICC for each patient. Seven prognostic predictors, including total bilirubin, tumor numbers, CEA, CA19-9, GGT, HBsAg and ISICC , were integrated into the final model. The C-index of the ISICC -applied prediction model was 0.719 (95% CI, 0.660-0.777) in the derivation cohort and 0.667 (95% CI, 0.581-0.732) in the validation cohort. Compared with the conventional staging systems, the new model presented better homogeneity and a lower Akaike information criteria value in ICC. The ISICC -applied prediction model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice.
| S-EPMC7156843 | biostudies-literature
Project description:The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.
| S-EPMC9663731 | biostudies-literature
Project description:We present a structured approach to combine explainability of artificial intelligence (AI) with the scientific method for scientific discovery. We demonstrate the utility of this approach in a proof-of-concept study where we uncover biomarkers from a convolutional neural network (CNN) model trained to classify patient sex in retinal images. This is a trait that is not currently recognized by diagnosticians in retinal images, yet, one successfully classified by CNNs. Our methodology consists of four phases: In Phase 1, CNN development, we train a visual geometry group (VGG) model to recognize patient sex in retinal images. In Phase 2, Inspiration, we review visualizations obtained from post hoc interpretability tools to make observations, and articulate exploratory hypotheses. Here, we listed 14 hypotheses retinal sex differences. In Phase 3, Exploration, we test all exploratory hypotheses on an independent dataset. Out of 14 exploratory hypotheses, nine revealed significant differences. In Phase 4, Verification, we re-tested the nine flagged hypotheses on a new dataset. Five were verified, revealing (i) significantly greater length, (ii) more nodes, and (iii) more branches of retinal vasculature, (iv) greater retinal area covered by the vessels in the superior temporal quadrant, and (v) darker peripapillary region in male eyes. Finally, we trained a group of ophthalmologists (N=26) to recognize the novel retinal features for sex classification. While their pretraining performance was not different from chance level or the performance of a nonexpert group (N=31), after training, their performance increased significantly (p<0.001, d=2.63). These findings showcase the potential for retinal biomarker discovery through CNN applications, with the added utility of empowering medical practitioners with new diagnostic capabilities to enhance their clinical toolkit.
| S-EPMC10517742 | biostudies-literature
Project description:Background and aimsTumor recurrence is frequent even in intrahepatic cholangiocarcinoma (ICC), and improved strategies are needed to identify patients at highest risk for such recurrence. We performed genome-wide expression profile analyses to discover and validate a gene signature associated with recurrence in patients with ICC.Approach and resultsFor biomarker discovery, we analyzed genome-wide transcriptomic profiling in ICC tumors from two public data sets: The Cancer Genome Atlas (n = 27) and GSE107943 (n = 28). We identified an eight-gene panel (BIRC5 [baculoviral IAP repeat containing 5], CDC20 [cell division cycle 20], CDH2 [cadherin 2], CENPW [centromere protein W], JPH1 [junctophilin 1], MAD2L1 [mitotic arrest deficient 2 like 1], NEIL3 [Nei like DNA glycosylase 3], and POC1A [POC1 centriolar protein A]) that robustly identified patients with recurrence in the discovery (AUC = 0.92) and in silico validation cohorts (AUC = 0.91). We next analyzed 241 specimens from patients with ICC (training cohort, n = 64; validation cohort, n = 177), followed by Cox proportional hazard regression analysis, to develop an integrated transcriptomic panel and establish a risk-stratification model for recurrence in ICC. We subsequently trained this transcriptomic panel in a clinical cohort (AUC = 0.89; 95% confidence interval [CI] = 0.79-0.95), followed by evaluating its performance in an independent validation cohort (AUC = 0.86; 95% CI = 0.80-0.90). By combining our transcriptomic panel with various clinicopathologic features, we established a risk-stratification model that was significantly superior for the identification of recurrence (AUC = 0.89; univariate HR = 6.08, 95% CI = 3.55-10.41, P < 0.01; and multivariate HR = 3.49, 95% CI = 1.81-6.71, P < 0.01). The risk-stratification model identified potential recurrence in 85% of high-risk patients and nonrecurrence in 76% of low-risk patients, which is dramatically superior to currently used pathological features.ConclusionsWe report a transcriptomic signature for risk-stratification and recurrence prediction that is superior to currently used clinicopathological features in patients with ICC.
| S-EPMC8443691 | biostudies-literature
Project description:PurposeThe present study assesses long-term overall survival (OS) and disease-free survival (DFS) after curative resection for intrahepatic cholangiocarcinoma (ICCA) depending on resection margin (RM) status and lymph node (LN) status.MethodsClinical data of all consecutively resected patients with ICCA at a single high-volume center between 2005 and 2018 were collected. Minimum follow-up was 36 months. Perioperative and long-term oncological outcome was assessed.ResultsOne hundred ninety-two cases were included in the analysis. Thirty- and 90-day-mortality was 5.2% (n = 10) and 10.9% (n = 21). OS was 26 months with 1-, 2-, and 5-year-OS rates of 72%, 53%, and 26%. One-, 2-, and 5-year-DFS rates were 54%, 42%, and 35% (N0 vs. N1: 29 vs. 9 months, p = 0.116). R1 was not found to be an independent risk factor for reduced survival in the overall cohort (p = 0.098). When differentiating according to the LN status, clear resection margins were significantly associated with increased DFS for N0 cases (50 months vs. 9 months, p = 0.004). For N1 cases, no significant difference in DFS was calculated for R0 compared to R1 cases (9 months vs. 9 months, p = 0.88). For N0 cases, clear resection margins > 10 mm were associated with prolonged OS (p = 0.048).ConclusionFor N1 cases, there was no significant survival benefit when comparing R0 versus R1, while the complication rate remained high for the extended resection types. In view of merging multimodal treatment, the hilar first concept assesses locoregional LN status for optimal surgical therapy.
| S-EPMC10404569 | biostudies-literature
Project description:The increasing availability of personal genome data has led to escalating needs by consumers to understand the implications of their gene sequences. At present, poorly integrated genetic knowledge has not met these needs. This proof-of-concept study proposes a similarity-based approach to assess the disease risk predisposition for personal genomes. We hypothesize that the semantic similarity between a personal genome and a disease can indicate the disease risks in the person. We developed a knowledge network that integrates existing knowledge of genes, diseases, and symptoms from six sources using the Semantic Web standard, Resource Description Framework (RDF). We then used latent relationships between genes and diseases derived from our knowledge network to measure the semantic similarity between a personal genome and a genetic disease. For demonstration, we showed the feasibility of assessing the disease risks in one personal genome and discussed related methodology issues.
| S-EPMC3243222 | biostudies-literature
Project description:BACKGROUND:The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using. METHODS:Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes. RESULTS:Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI. CONCLUSIONS:The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.
| S-EPMC6609928 | biostudies-other
Project description:Background and objectivesInvestigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.Patients and methodsThe retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems.ResultsThe median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.ConclusionsOur AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.
| S-EPMC9978529 | biostudies-literature
Project description:Reviews have traditionally been based on extensive searches of the available bibliography on the topic of interest. However, this approach is frequently influenced by the authors' background, leading to possible selection bias. Artificial intelligence applied to natural language processing (NLP) is a powerful tool that can be used for systematic reviews by speeding up the process and providing more objective results, but its use in scientific literature reviews is still scarce. This manuscript addresses this challenge by developing a reproducible tool that can be used to develop objective reviews on almost every topic. This tool has been used to review the antibacterial activity of Cistus genus plant extracts as proof of concept, providing a comprehensive and objective state of the art on this topic based on the analysis of 1601 research manuscripts and 136 patents. Data were processed using a publicly available Jupyter Notebook in Google Collaboratory here. NLP, when applied to the study of antibacterial activity of Cistus plants, is able to recover the main scientific manuscripts and patents related to the topic, avoiding any biases. The NLP-assisted literature review reveals that C. creticus and C. monspeliensis are the first and second most studied Cistus species respectively. Leaves and fruits are the most commonly used plant parts and methanol, followed by butanol and water, the most widely used solvents to prepare plant extracts. Furthermore, Staphylococcus. aureus followed by Bacillus. cereus are the most studied bacterial species, which are also the most susceptible bacteria in all studied assays. This new tool aims to change the actual paradigm of the review of scientific literature to make the process more efficient, reliable, and reproducible, according to Open Science standards.
| S-EPMC9952093 | biostudies-literature
Project description:BackgroundThe effectiveness of adjuvant transarterial chemoembolization (TACE) for intrahepatic cholangiocarcinoma (ICC) after hepatectomy remains unclear. This study was performed to identify ICC patients who would benefit from adjuvant TACE.Patients and methodsThe study included 553 patients who underwent hepatectomy for ICC between January 2008 and February 2011 at the Eastern Hepatobiliary Surgery Hospital and who were treated with or without TACE (122 with TACE and 431 without TACE). Survival risk stratification was performed using the established prognostic nomogram (ICC nomogram). The predictive performance was evaluated by concordance index and calibration. The tumor recurrence and overall survival (OS) rates were analyzed by the Kaplan-Meier method before and after propensity score matching (PSM).ResultsThe predictive performance of the ICC nomogram was demonstrated by the well-fitted calibration curves and an optimal c-index of 0.71 for OS prediction. In the whole cohort, the 5-year recurrence and OS rates between the TACE and non-TACE groups were significantly different (5-year recurrence: 72.9% vs. 78.1%; OS: 38.4% vs. 29.7%). After 1:1 PSM, the TACE and non-TACE groups (122 patients each) had similar 5-year recurrence and OS rates (5-year recurrence: 72.9% vs. 74.2%; OS: 38.4% vs. 36.0%). By survival risk stratification based on ICC nomogram, only the patients in the lowest tertile (nomogram scores ?77) benefited from adjuvant TACE (TACE vs. non-TACE groups: 90.4% vs. 95.9% for 5-year recurrence; 21.3% vs. 6.2% for 5-year OS).ConclusionAdjuvant TACE following liver resection might be suitable for ICC patients with high ICC nomogram scores (?77).Implications for practiceThe accurate predictive performance of the established prognostic nomogram for intrahepatic cholangiocarcinoma (ICC) following liver resection was reconfirmed in an independent cohort with 553 patients. Based on the survival risk stratification using the nomogram, adjuvant transarterial chemoembolization following liver resection might be suitable only for ICC patients with high scores from the nomogram.
| S-EPMC4571785 | biostudies-literature