scholarly journals FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1419-1429 ◽  
Author(s):  
Wei Lu ◽  
Dongliang Fu ◽  
Xiangxing Kong ◽  
Zhiheng Huang ◽  
Maxwell Hwang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Treatment Response ◽  
Cancer Patients ◽  
Learning Algorithms ◽  
Response Prediction ◽  
Machine Learning Algorithms ◽  
Recurrent Colorectal Cancer ◽  
Colorectal Cancer Patients ◽  
Treatment Response Prediction

scholarly journals Monitoring treatment response in metastasic colorectal cancer: Economic evaluation of PrediCTC versus computed tomography scan

2019 ◽  
Vol 2019 ◽  
pp. 228424031985833
Author(s):  
Cristina Antón Rodríguez ◽  
Miguel Abal Posada ◽  
Lorena Alonso Alconada ◽  
Sonia Candamio Folgar ◽  
Rafael López López ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Computed Tomography ◽  
Side Effects ◽  
Adverse Events ◽  
Treatment Response ◽  
Cancer Patients ◽  
Societal Perspective ◽  
Cost Utility ◽  
Life Years ◽  
Colorectal Cancer Patients

Background: Late state colorectal cancer treatments have important side effects that should be avoided in patients where drug effectiveness is not adequate. PrediCTC is a new biomarkers blood test developed to determinate the chemotherapy response in unresectable metastatic colorectal cancer patients that could allow to obviate unnecessary treatments. Aim: To assess from the Spanish Societal Perspective the cost-utility of the test PrediCTC compared to the computed tomography in aim to evaluate chemotherapy treatment response in late stage colorectal cancer patients. Methods: Based on the results of Barbazán et al., a Markov model has been developed, in which the different lines and cycles that the colorectal patient can receive and how they can move between them according to the computed tomography or PrediCTC have been represented. The effectiveness has been expressed in quality adjusted life years (QALYs), avoiding adverse events. Results: Base case analysis shows savings in different types of costs for PrediCTC (per patient): €14.30 in those arise from adverse events, €22,345.73 in chemotherapy costs, €4849.61 in other direct costs, and €306.21 in indirect costs. Although computed tomography 12-week assessed patients gain 0.17 QALYs compared with PrediCTC. Conclusions: From the Spanish Societal Perspective, PrediCTC is not a cost-utility option but allows to identify earlier patients who are not benefiting from first-line chemotherapy avoiding unnecessary side effects and costs.

scholarly journals KCNB1 gene polymorphisms and related indel as predictor biomarkers of treatment response for colorectal cancer – toward a personalized medicine

Tumor Biology ◽  
2020 ◽  
Vol 42 (6) ◽  
pp. 101042832092523 ◽  
Author(s):  
Mouadh Barbirou ◽  
Ikram Sghaier ◽  
Sinda Bedoui ◽  
Rahma Ben Abderrazek ◽  
Hazar Kraiem ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Treatment Response ◽  
Cancer Patients ◽  
Serum Levels ◽  
Amplification Refractory Mutation System ◽  
Specific Response ◽  
Tumor Localization ◽  
Gene Variants ◽  
Treatment And Prevention ◽  
Colorectal Cancer Patients

The KCNB1 gene variants were differentially associated with cancers. However, their association with colorectal cancer has not yet been explored. We investigated the contribution of the KCNB1 gene variants rs3331, rs1051295, and indel (insertion/deletion) rs11468831 Polymorphism as predictors of the treatment response in colorectal cancer patients. A retrospective study, which involved 291 Tunisian colorectal cancer patients (aged 60.0 ± 13.1 years), who were stratified into responder and non-responder groups, according to TNM stages and their responsiveness to chemotherapy based on fluorouracil. KCNB1 genotyping was performed with amplification-refractory mutation system–polymerase chain reaction, and was confirmed by Sanger sequencing. Sex-specific response was found and colorectal cancer females are less likely to achieve a positive response during the chemotherapy strategy, compared to males. Weight and body mass index, tumor size, and tumor localization are considered as predictive factors to treatment responsiveness. Carriage of rs11468831 Ins allele was significantly associated with successful therapy achievement ( p adjusted < 0.001). Stratification of colorectal cancer patients’ response according to tumor localization and TNM stages reveals negative association of rs3331 Major allele to treatment response among the patients with advanced cancer stages (subgroup G2). The presence of rs3331 (homozygous minor) C/C genotype was positively associated with decline in carcino-embryonic antigen ( p = 0.043) and CA19-9 ( p = 0.014) serum levels. On the other hand, the presence of rs1051295 (homozygous minor) A/A genotype was correlated with marked decline in CA19-9 serum levels. KCNB1 haplotype did not reveal any association between haplotypes and treatment response. The results obtained suggest that gender-specific strategies for screening treatment and prevention protocols as well as KCNB1 variants may constitute an effective model for ongoing personalization medicine.

scholarly journals Molecular detection of p16 promoter methylation in the serum of recurrent colorectal cancer patients

2003 ◽  
Vol 105 (4) ◽  
pp. 491-493 ◽  
Author(s):  
Hiroshi Nakayama ◽  
Kenji Hibi ◽  
Tsunenobu Takase ◽  
Taiji Yamazaki ◽  
Yasushi Kasai ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Promoter Methylation ◽  
Molecular Detection ◽  
Recurrent Colorectal Cancer ◽  
Colorectal Cancer Patients

scholarly journals A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1248
Author(s):  
Eleana Hatzidaki ◽  
Aggelos Iliopoulos ◽  
Ioannis Papasotiriou
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Cancer Screening ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Significant Information

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

scholarly journals Machine Learning Algorithms and Whole Exome Sequencing Data from Breast Cancer Patients in the UK Biobank Predict Survival

2020 ◽  
Author(s):  
Bum-Sup Jang ◽  
In Ah Kim
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Overall Survival ◽  
Cancer Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Uk Biobank ◽  
Breast Cancer Patients ◽  
Gene Set ◽  
The Uk

Abstract Background: Using by machine learning algorithms, we aimed to identify the mutated gene set from the whole exome sequencing (WES) data of blood in the cancer, which is associated with overall survival in breast cancer patients.Methods: WES data from 1,181 female breast cancer patients within the UK Biobank cohort was collected. The number of mutations for each gene was summed and defined as the blood-based mutation burden per patient. Using by Long short-term memory (LSTM) machine learning algorithm and a XGBoost—a gradient-boosted tree algorithm, we developed the model to predict patient overall survival. Results: From the UK biobank-breast cancer cohort, most altered genes in blood samples were related with the TP53 pathway. In the LSTM model, the minimum 50 genes were found to predict high vs. low mutation burden. In the XGBoost survival model, the gene-set could predict overall survival showing the concordance index of 0.75 and the scaled Brier-score of 0.146 from the held-out testing set (20%, N=236). In older patients (≥ 56 years), the high mutation group based on this gene-set showed inferior overall survival compared to the low mutation group (log-rank test, P=0.042)Conclusion: The machine learning algorithms revealed the gene-signature in the UK biobank breast cancer cohort. Mutational burden observed in blood was associated with overall survival in relatively old patients. This gene-signature should be verified in prospective setting.

scholarly journals Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

JAMIA Open ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Meijian Guan ◽  
Samuel Cho ◽  
Robin Petro ◽  
Wei Zhang ◽  
Boris Pasche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Patients ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Treatment Change ◽  
Progress Notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

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