Overall and Disease-Free Survival Prediction of Postoperative Breast Cancer Patients using Machine Learning Techniques

Shouket, Tahreem; Mahmood, Sajid; Hassan, Malik Tahir; Iftikhar, Afnan

doi:10.1109/inmic48123.2019.9022756

Cited by 4 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the class imbalance, 24 studies showed class imbalance in the samples of the final model construction [2, 23, 24, 26, 28-37, 40-45, 47-50], and 7 of them dealt with this problem [28,29,34,37,42,47,49]. The methods included undersampling, bagging algorithm, SMOTE, PSO, K-means, KNN, and bagging.…”

Section: Data Preparation and Modelingmentioning

confidence: 99%

“…For model presentation, 6 studies were presented as formulas [23,29,35,36,39,44], 5 as graphs [24,38,47,48,52], and 16 as a combination of formulas and graphs [2, 25, 27, 30-32, 34, 40-43, 45, 46, 49-51]. Models were not presented in 4 studies [26,28,33,37].…”

Section: Data Preparation and Modelingmentioning

confidence: 99%

“…conducted in only one study [40], and model calibration was performed in only 1 study [48]. [26-34, 36, 39-43, 45, 47-49, 51], ranging from 0.500 to 0.972; 6 studies evaluated the precision/positive predictive value [23,40,41,[46][47][48], ranging from 0.549 to 1; 5 studies evaluated the F1 score [43,[45][46][47]51], ranging from 0.369 to 0.966; 5 studies evaluated Mcc [40,41,[46][47][48], ranging from 0 to 0.884; 2 studies evaluated the NPV [46,47], ranging from 0 to 1; and 2 studies evaluated the G-mean [29,34], ranging from 0.334 to 0.959.…”

Section: Plos Onementioning

confidence: 99%

“…In studies that compared of two or more algorithms, ANN had the best performance in 6 studies [27,34,39,46,49,52], DT had the best performance in 4 studies [2,23,26,34], the ensemble learning algorithm had the best performance in 4 studies [42,43,48,51], semisupervised learning had the best performance in 3 studies [30][31][32], DNN had the best performance in 3 studies [40,41,45], SVM had the best performance in 2 studies [35,37], LR had the best performance in 2 studies [24,29], KNN had the best performance in 1 study [36], and Naive Bayes had the best performance in 1 study [47] (see S5 Table ).…”

Section: Plos Onementioning

confidence: 99%

“…The median number of candidate predictors used was 16 (range: 3~625); 29 studies used only clinical data [2,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][42][43][44][45][46][47][48][49][50][51][52], 1 study combined clinical data with molecular data for prediction [40], and 1 study combined clinical data, molecular data and pathological image data for prediction [41]. We ranked the frequency of the use of certain predictors from high to low.…”

Section: Candidate Predictorsmentioning

confidence: 99%

See 4 more Smart Citations

Predicting breast cancer 5-year survival using machine learning: A systematic review

Zhou

Dong

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

Background Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Results Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Conclusions Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.

show abstract

Section: Data Preparation and Modelingmentioning

confidence: 99%

Section: Data Preparation and Modelingmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Candidate Predictorsmentioning

confidence: 99%

See 3 more Smart Citations

Predicting breast cancer 5-year survival using machine learning: A systematic review

Zhou

Dong

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

Breast Cancer Survival Prediction and Analysis

Peddarapu,

Nunemuntala,

Koora

et al. 2023

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)

View full text Add to dashboard Cite

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients

2023

View full text Add to dashboard Cite

The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs. In the I-SPY-1 TRIAL, 155 patients with stage 2 or 3 breast cancer with breast tumors underwent neoadjuvant chemotherapy met the inclusion/exclusion criteria. The inputs were dynamic-contrast-enhanced (DCE) MRI, and T2- weighted MRI as three-dimensional whole-images without the tumor segmentation, as well as molecular subtypes and demographics. The outcomes were PCR, RCB, and PFS. Three (“Integrated”, “Stack” and “Concatenation”) CNN were evaluated using receiver-operating characteristics and mean absolute errors. The Integrated approach outperformed the “Stack” or “Concatenation” CNN. Inclusion of both MRI and non-MRI data outperformed either alone. The combined pre- and post-neoadjuvant chemotherapy data outperformed either alone. Using the best model and data combination, PCR prediction yielded an accuracy of 0.81±0.03 and AUC of 0.83±0.03; RCB prediction yielded an accuracy of 0.80±0.02 and Cohen’s κ of 0.73±0.03; PFS prediction yielded a mean absolute error of 24.6±0.7 months (survival ranged from 6.6 to 127.5 months). Deep learning using longitudinal multiparametric MRI, demographics, and molecular subtypes accurately predicts PCR, RCB, and PFS in breast cancer patients. This approach may prove useful for treatment selection, planning, execution, and mid-treatment adjustment.

show abstract

Overall and Disease-Free Survival Prediction of Postoperative Breast Cancer Patients using Machine Learning Techniques

Cited by 4 publications

References 11 publications

Predicting breast cancer 5-year survival using machine learning: A systematic review

Predicting breast cancer 5-year survival using machine learning: A systematic review

Breast Cancer Survival Prediction and Analysis

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients

Contact Info

Product

Resources

About