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Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.

ABSTRACT: Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining. Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1?, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02). Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.

SUBMITTER: Cai H

PROVIDER: S-EPMC6603411 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.

Cai Hongmin H Pang Xiaolin X Dong Dong D Ma Yan Y Huang Yan Y Fan Xinjuan X Wu Peihuang P Chen Haiyang H He Fang F Cheng Yikan Y Liu Shuai S Yu Yizhen Y Hong Minghuang M Xiao Jian J Wan Xiangbo X Lv Yanchun Y Zheng Jian J

Journal of Cancer 20190602 15

<b>Background</b>: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. <b>Methods</b>: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient ...[more]

PMID: 31293635

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Project description:BackgroundDengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.Methods and findingsA total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm(3) or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.ConclusionThis study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.

Project description:PurposeConcurrent chemo radiotherapy (CCRT) has been the standard of care in locally advanced nasopharyngeal carcinoma (LA-NPC) for many years. The role of induction chemotherapy (ICT) has always been controversial. This systematic review and meta-analysis investigates the value of adding ICT to CCRT in LA-NPC.Materials and methodsTwo reviewers independently assessed the eligibility of randomized controlled trials (RCTs) comparing ICT followed by CCRT versus CCRT alone, including treatment-naive adult patients with histologically proven nonmetastatic LA-NPC.ResultsEight RCTs with in total 2,384 randomized patients, of whom 69% had N2-N3 disease, were selected. ICT was the allocated treatment in 1,200 patients, of whom 1,161 actually received this. Treatment compliance varied, with a median rate of 92% (range, 86%-100%) of patients receiving all cycles of ICT. The percentage of patients completing radiotherapy was 96% and 95% [(Combined Risk difference(CRD)= 0.004; 95% Confidence Interval (CI) -0.001-0.01; p = 0.14)] in the ICT group and CCRT group, respectively, whereas chemotherapy during radiotherapy could be completed in only 28% of the ICT group versus 61% in the CCRT group (CRD, -0.243; 95% CI, -0.403 to -0.083; p = .003). Grade 3-4 acute toxicity was mostly hematologic during the ICT phase (496 events vs. 191 nonhematologic) and was predominant in the ICT group (1,596 events vs. 1,073 in the CCRT alone group) during the CCRT. Adding ICT to CCRT provided a significant benefit in overall survival (hazard ratio [HR], 0.680; 95% CI, 0.511-0.905; p = .001) and progression-free survival (HR, 0.657; 95% CI, 0.568-0.760; p < .001).ConclusionAlthough ICT followed by CCRT is associated with more acute toxicity and a lower compliance of the chemotherapy during the CCRT phase, this association resulted in a clinically meaningful survival benefit. ICT should be considered as a standard option in patients with LA-NPC, but further study on optimal patient selection for this treatment is warranted.Implications for practiceLocally advanced nasopharyngeal carcinoma (LA-NPC) is a relatively common disease in some parts of the world, with a rather poor prognosis due to its high metastatic potential. The role of induction chemotherapy (ICT) has always been controversial. This meta-analysis found that ICT followed by concurrent chemoradiotherapy (CCRT) in LA-NPC is associated with a significant clinical improvement in both overall survival and progression-free survival compared with CCRT alone. ICT should be considered as a standard option in patients with LA-NPC.

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Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.

Publications

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.

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