Dataset Information

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.

ABSTRACT: Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.

SUBMITTER: Muller L

PROVIDER: S-EPMC8150393 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.

Müller Lukas L Mähringer-Kunz Aline A Gairing Simon Johannes SJ Foerster Friedrich F Weinmann Arndt A Bartsch Fabian F Heuft Lisa-Katharina LK Baumgart Janine J Düber Christoph C Hahn Felix F Kloeckner Roman R

Journal of clinical medicine 20210512 10

Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Establish ...[more]

PMID: 34066001

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Project description:PurposeAutomation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup.MethodsAn automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day's registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance.ResultsFollowing an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.ConclusionIn this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.

Project description:BackgroundEfficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study.MethodsThe study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression.FindingsAmong the 2810 participants (mean Age = 68.5 ± 9.3 years, average Follow-up = 5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index = 0.72, 95% CI: 0.70-0.74), outperforming that of the known risk factors (c-index = 0.59, 95% CI 0.57-0.61).InterpretationThis proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer.FundingThe Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

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.

Dataset Information

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.

Publications

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.

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