Dataset Information

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

ABSTRACT: Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.

SUBMITTER: Baig Z

PROVIDER: S-EPMC8573477 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature

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Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

Baig Zarrukh Z Abu-Omar Nawaf N Khan Rayyan R Verdiales Carlos C Frehlick Ryan R Shaw John J Wu Fang-Xiang FX Luo Yigang Y

Technology in cancer research & treatment 20210101

<b>Background:</b> The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. <b>Methods:</b> A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = ...[more]

PMID: 34738844

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Project description:ImportancePatient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients.ObjectiveTo develop and validate a machine learning approach to predict future ED/Hosp in patients with head and neck cancer.Design, setting, and participantsThis was a population-based predictive modeling study of patients in Ontario, Canada, diagnosed with head and neck cancer from January 2007 through March 2018. All outpatient clinical encounters were identified. Edmonton Symptom Assessment System (ESAS) scores and clinical and demographic factors were abstracted. Training and test cohorts were randomly generated in a 4:1 ratio. Various machine learning algorithms were explored, including (1) logistic regression using a least absolute shrinkage and selection operator, (2) random forest, (3) gradient boosting machine, (4) k-nearest neighbors, and (5) an artificial neural network. Data analysis was performed from September 2021 to January 2022.Main outcomes and measuresThe main outcome was any 14-day ED/Hosp event following symptom assessment. The performance of each model was assessed on the test cohort using the area under the receiver operator characteristic (AUROC) curve and calibration plots. Shapley values were used to identify the variables with greatest contribution to the model.ResultsThe training cohort consisted of 9409 patients (mean [SD] age, 63.3 [10.9] years) undergoing 59 089 symptom assessments (80%). The remaining 2352 patients (mean [SD] age, 63.3 [11] years) and 14 193 symptom assessments were set aside as the test cohort (20%). Several models had high predictive accuracy, particularly the gradient boosting machine (validation AUROC, 0.80 [95% CI, 0.78-0.81]). A Youden-based cutoff corresponded to a validation sensitivity of 0.77 and specificity of 0.66. Patient-reported symptom scores were consistently identified as being the most predictive features within models. A second model built only with symptom severity data had an AUROC of 0.72 (95% CI, 0.70-0.74).Conclusions and relevanceIn this study, machine learning approaches predicted with a high degree of accuracy ED/Hosp in patients with head and neck cancer. These tools could be used to accurately risk stratify patients and may help direct targeted intervention.

Project description:BackgroundRadiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI.PurposeThe purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images.Materials and methodsFDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately.ResultsCNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours.ConclusionHigh performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

Project description:BackgroundEarly childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management is limited. The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC.MethodsThis study was a secondary analysis of a recently published clinical trial that recruited 1,070 children aged 3- to 4-year-old with ECC. Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. Candidate predictors included clinical parameters (caries experience and oral hygiene status), oral health-related behaviours (toothbrushing habits, feeding history and snacking preference) and socioeconomic backgrounds of the children. Model performance was evaluated using discrimination and calibration metrics including accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUROC) and Brier score. Shapley additive explanations were deployed to identify the important predictors.ResultsAll machine learning models showed good performance in predicting the treatment outcome of ECC. The accuracy, recall, precision, F1 score, AUROC, and Brier score of the six models ranged from 0.674 to 0.740, 0.731 to 0.809, 0.762 to 0.802, 0.741 to 0.804, 0.771 to 0.859, and 0.134 to 0.227, respectively. The important predictors of the caries-arresting outcome were the surface and tooth location of the carious lesions, newly developed caries during follow-ups, baseline caries experience, whether the children had assisted toothbrushing and oral hygiene status.ConclusionsMachine learning can provide promising predictions of the treatment outcome of ECC. The identified key predictors would be particularly informative for targeted management of ECC.

Project description:ImportancePostoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.ObjectiveTo evaluate whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation.Design, setting, and participantsThis cohort study included patients diagnosed with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx from January 1, 2004, through December 31, 2016. Patients had resected disease and underwent adjuvant radiotherapy. Analysis was performed from October 1, 2019, through September 1, 2020. Patients were selected from the National Cancer Database, a hospital-based registry that captures data from more than 70% of newly diagnosed cancers in the United States. Three machine learning survival models were trained using 80% of the cohort, with the remaining 20% used to assess model performance.ExposuresReceipt of adjuvant chemoradiation or radiation alone.Main outcomes and measuresPatients who received treatment recommended by machine learning models were compared with those who did not. Overall survival for treatment according to model recommendations was the primary outcome. Secondary outcomes included frequency of recommendation for chemotherapy and chemotherapy benefit in patients recommended for chemoradiation vs radiation alone.ResultsA total of 33 527 patients (24 189 [72%] men; 28 036 [84%] aged ≤70 years) met the inclusion criteria. Median follow-up in the validation data set was 43.2 (interquartile range, 19.8-65.5) months. DeepSurv, neural multitask logistic regression, and survival forest models recommended chemoradiation for 17 589 (52%), 15 917 (47%), and 14 912 patients (44%), respectively. Treatment according to model recommendations was associated with a survival benefit, with a hazard ratio of 0.79 (95% CI, 0.72-0.85; P < .001) for DeepSurv, 0.83 (95% CI, 0.77-0.90; P < .001) for neural multitask logistic regression, and 0.90 (95% CI, 0.83-0.98; P = .01) for random survival forest models. No survival benefit for chemotherapy was seen for patients recommended to receive radiotherapy alone.Conclusions and relevanceThese findings suggest that machine learning models may identify patients with intermediate risk who could benefit from chemoradiation. These models predicted that approximately half of such patients have no added benefit from chemotherapy.

Dataset Information

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

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Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

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