Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

Rabinovici-Cohen, Simona; Rejo, Beatriz Grandal; Hexter, Efrat; Cubelos, Oliver Hijano; Pajula, Juha; Pölönen, Harri; Reyal, Fabien; Rosen-Zvi, Michal

doi:10.3390/cancers14163848

Cited by 18 publications

(10 citation statements)

References 44 publications

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“…The model revealed that k-nearest neighbour (KNN) and predictive mean matching (PMM) imputations provide close maximum area under ROC curves, 0.6428 (at 60% missingness with MAR mechanism) and 0.6418 (at 30% of missing data with MCAR mechanism) respectively. A study [ 41 ] demonstrated the ability to predict or discriminate the recurrence from non-recurrence cases of breast cancer prior to ‘neo-adjuvant chemotherapy’ treatment yield area under ROC curve of 75%; a difference of about 11% compared to ours. This implies that the imputed data via KNN and PMM are most plausible to enable classifiers to separate breast cancer recurrence cases for at least 64%.…”

Section: Discussionmentioning

confidence: 71%

Classification of breast cancer recurrence based on imputed data: a simulation study

Abassi

Msengwa

2022

BioData Mining

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Several studies have been conducted to classify various real life events but few are in medical fields; particularly about breast recurrence under statistical techniques. To our knowledge, there is no reported comparison of statistical classification accuracy and classifiers’ discriminative ability on breast cancer recurrence in presence of imputed missing data. Therefore, this article aims to fill this analysis gap by comparing the performance of binary classifiers (logistic regression, linear and quadratic discriminant analysis) using several datasets resulted from imputation process using various simulation conditions. Our study aids the knowledge about how classifiers’ accuracy and discriminative ability in classifying a binary outcome variable are affected by the presence of imputed numerical missing data. We simulated incomplete datasets with 15, 30, 45 and 60% of missingness under Missing At Random (MAR) and Missing Completely At Random (MCAR) mechanisms. Mean imputation, hot deck, k-nearest neighbour, multiple imputations via chained equation, expected-maximisation, and predictive mean matching were used to impute incomplete datasets. For each classifier, correct classification accuracy and area under the Receiver Operating Characteristic (ROC) curves under MAR and MCAR mechanisms were compared. The linear discriminant classifier attained the highest classification accuracy (73.9%) based on mean-imputed data at 45% of missing data under MCAR mechanism. As a classifier, the logistic regression based on predictive mean matching imputed-data yields the greatest areas under ROC curves (0.6418) at 30% missingness while k-nearest neighbour tops the value (0.6428) at 60% of missing data under MCAR mechanism.

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Section: Discussionmentioning

confidence: 71%

Classification of breast cancer recurrence based on imputed data: a simulation study

Abassi

Msengwa

2022

BioData Mining

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“…Using multimodal data is another solution for improving the prediction performance. Recent studies [ 32 – 34 ] in this emerging field have shown accurate prediction ability has further improved. Future research should prioritize the incorporation of additional modalities to establish a comprehensive multimodal representation approach.…”

Section: Discussionmentioning

confidence: 99%

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning

Sun,

Shao,

Wan

et al. 2024

BMC Med Inform Decis Mak

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Objectives This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan. Methods This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. Results There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features’ clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. Conclusions The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.

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“…There was currently no study using the MARS model to predict the prognosis for BC patients, to the best of our knowledge. Several articles using different ML models to predict the prognosis of BC patients aroused controversy [ 7 – 9 , 14 – 16 , 20 , 25 – 28 , 57 ]. Firstly, Kate et al [ 7 ] believed that NB was better than DT and LR through the research on more than 160000 BC patients.…”

Section: Discussionmentioning

confidence: 99%

“…Since ML models were susceptible to factors such as data sources, input variables, and software, several articles using ML to predict the prognosis of BC patients were controversial [ 7 – 9 , 12 – 16 , 20 , 25 – 28 ]. Lotfnezhad Afshar et al [ 9 ] believed that support vector machine (SVM) model outperformed other models in the predicting the survival rate of BC patients.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

Luo

Wan

et al. 2023

PLoS ONE

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Introduction Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. Objective The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. Methods This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. Results Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820–0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). Conclusions The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

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Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

Cited by 18 publications

References 44 publications

Classification of breast cancer recurrence based on imputed data: a simulation study

Classification of breast cancer recurrence based on imputed data: a simulation study

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

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