Breast cancer data analysis for survivability studies and prediction

Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jie

doi:10.1016/j.cmpb.2017.12.011

Cited by 93 publications

(40 citation statements)

References 25 publications

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“…Using only a very limited set of clinical variables, we demonstrate that our models are capable of predicting meaningful clinical outcomes. Previous studies using the SEER database have used various machine learning methods for diagnosis and prognosis purposes in breast [11][12][13][14][15] and lung cancers, 16,17 but have not applied these techniques to the SEER data on meningiomas. As compared to classical statistical approaches, the value of predictive modeling is the ability to obtain predictions for individual patients rather than group means.…”

Section: Discussionmentioning

confidence: 99%

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

et al. 2020

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Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedure. We demonstrate that our models are capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across 16 SEER registries. A free smartphone and web application is provided for readers to access and test the predictive models (www.meningioma.app). Future model improvements and prospective replication will be necessary to demonstrate true clinical utility. Rather than being used in isolation, we expect that the proposed models will be integrated into larger and more comprehensive models that integrate imaging and molecular biomarkers. Whether for meningiomas or other tumors of the CNS, the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.npj Digital Medicine (2020) 3:12 ; https://doi.

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Section: Discussionmentioning

confidence: 99%

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

et al. 2020

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“…Breast cancer is the most common type of cancer affecting females worldwide (1). About one-half of all newly diagnosed breast cancer cases occur in women older than 65 years (2).…”

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confidence: 99%

Simultaneous Integrated Boost in Once-weekly Hypofractionated Radiotherapy for Breast Cancer in the Elderly: Preliminary Evidence

Bonzano

Belgioia

Polizzi

et al. 2019

In Vivo

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Aim: To evaluate once-weekly hypofractionated radiotherapy in elderly patients affected by early breast cancer, reporting acute and late toxicity profiles, and treatment feasibility. Patients and Methods: Fifty patients were treated with a hypofractionated regimen: 28.5±2.5 Gy in five fractions at one fraction weekly. Simultaneous integrated boost (SIB) to the tumor bed in high-risk cases. Inclusion criteria: patients over 70 years old, pT1-2, N0-1a. Acute and late toxicities were assessed based on Radiation Therapy Oncology Group. Results: The median follow-up was 20 months and the median patient age was 79 years. SIB was added for 22 patients (44%). Grade 3-4 acute cutaneous toxicities were not observed; grade 2 toxicity occurred only in four patients (8%). Late subcutaneous tissue toxicity consisted of grade 2 fibrosis in two patients (4%), grade 1 in five (10%) and grade 0 in 41(85%). Conclusion: Limiting fraction numbers with a safer profile may improve the management of breast cancer for the elderly.

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“…Shukla et al [7], invents a new technique for patient survivability in the presence of missing data and also frame cohorts of breast cancer patients that contribute exact features by using SOM & DBSCAN i.e unsupervised data mining methods to create a patient cohort clusters [8] and decision tree gives the best performance and generalized into one of cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Accurate screening has lots of chances to be cured and the diagnosis of BC in screening helps a doctor for prediction analysis with the help of data mining. After studies, the number of papers based on BC research, this paper is going to be proposed the algorithm for attribute filtration method and the implementation by different classifier like naive bayes, decision tree, svm, k-nn with the aim to get the best output with less number of an attribute [5,6,7,10,11,12]. The Proposed model as shown in Fig.1 is consists of several steps and each step is discussed below-…”

Section: Proposed Modelmentioning

confidence: 99%

An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining

Sinha¹,

Sahoo²,

Rautaray³

et al. 2019

IJIEEB

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This presented research paper mainly studies the frequent itemsets mining approach for finding the most important attribute to overcome the existing problems in the extraction of relevant information by using data mining approaches from a huge amount of dataset. Firstly a state of art diagram for prediction is designed and data mining classifier like naive bayes, support vector machine, decision tree, k-nearest neighbour are compared and then proposed methodology with new techniques are proposed. Moreover, a new attribute filtering association frequent itemsets mining algorithm is presented. Then, by analyzing the feasibility of the proposed algorithm, the data mining classification classifier is compared. As a result, SVM produces the best result among all the classifier with attribute filtrating and without attribute filtrating. With attribute filtrating algorithm enhances the accuracy of all the other classifier.

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Breast cancer data analysis for survivability studies and prediction

Cited by 93 publications

References 25 publications

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

Simultaneous Integrated Boost in Once-weekly Hypofractionated Radiotherapy for Breast Cancer in the Elderly: Preliminary Evidence

An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining

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