Predicting factors influencing survival of breast cancer patients using logistic regression of machine learning

2021 ◽  
Author(s):  
Huda Kutrani ◽  
Saria Eltalhi ◽  
Naeima Ashleik
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Predicting Factors ◽  
Factors Influencing

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

scholarly journals Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 301
Author(s):  
Amal Ahmed Abd El-Fattah ◽  
Nermin Abdel Hamid Sadik ◽  
Olfat Gamil Shaker ◽  
Amal Mohamed Kamal ◽  
Nancy Nabil Shahin
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Breast Cancer Diagnosis ◽  
Serum Levels ◽  
Breast Cancer Patients ◽  
Multivariate Logistic Regression ◽  
Expression Levels ◽  
Non Coding Rnas ◽  
Pai 1

Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.

scholarly journals Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongwei Yu ◽  
Xianqi Meng ◽  
Huang Chen ◽  
Jian Liu ◽  
Wenwen Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Training Dataset ◽  
Validation Dataset ◽  
Gray Level ◽  
Breast Cancer Patients ◽  
Short Run

ObjectivesThis study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer.MethodsData from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups.ResultsAmong the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively).ConclusionRadiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.

Factors influencing options in primary breast cancer treatment.

1987 ◽  
Vol 5 (1) ◽  
pp. 68-74 ◽  
Author(s):  
W H Wolberg ◽  
M A Tanner ◽  
E P Romsaas ◽  
D L Trump ◽  
J F Malec
Keyword(s):  
Breast Cancer ◽  
Cancer Treatment ◽  
Cancer Patients ◽  
Primary Breast Cancer ◽  
Breast Cancer Treatment ◽  
Cancer Information ◽  
Breast Cancer Patients ◽  
Term Survival ◽  
Tumor Excision ◽  
Factors Influencing

Primary breast cancer treatment is determined by tumor factors and by patient preference. Breast cancer treatments that preserve the cosmetic appearance of the breast are appealing and effective for appropriately selected patients; long-term survival following tumor excision and breast irradiation appears to be comparable to that for mastectomy. Since April 1981, when a protocol was developed and treatment options were offered, factors influencing treatment selection have been analyzed in 206 consecutive primary breast cancer patients. Mastectomy was dictated by tumor-related factors in 96 patients (47%); 110 patients (53%) had the option of mastectomy or conservation--tumor excision plus radiotherapy to the breast. Among these 110 eligible patients, 54 chose conservation (49%) and 56 chose mastectomy (51%). Intraoperative findings for ten patients electing conservation necessitated mastectomy, so conservation was accomplished for 44 (21%) of those treated for breast cancer. Beginning in July 1982, breast cancer patients took a battery of psychosexual assessments before any operation (Profile of Mood States [POMS], Health Locus of Control Scale [HLCS] Locke-Wallace Marital Adjustment Test [MAT], Psychosocial Adjustment to Illness Scale [PAIS], Derogatis Sexual Function Inventory [DSFI], Millon Clinical Multiaxial Inventory [MCMI], and a Breast Cancer Information Test [BCIT]). Comparisons of psychologic and demographic variables were made between patients who chose mastectomy and those who chose conservation. No demographic variable was statistically significantly related to choice, although older women tended to select mastectomy more than younger women. Compared with those who elected conservation, women who elected mastectomy were more tense and anxious (P less than .01), more introverted (P less than .01), felt more depressed and dejected (P less than .05), and reported more sexual problems (P less than .05). Those who elected conservation valued their physical appearance more highly (P less than .01) and were generally more self-interested (P less than .05). Mastectomy was dictated by medical considerations for approximately half of patients with breast cancer. Among candidates for breast conservation, the importance of retaining the breast appeared to be determined to a significant degree by measurable psychological factors.

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