Dataset Information

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

ABSTRACT: Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.

SUBMITTER: Massafra R

PROVIDER: S-EPMC9484691 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Massafra Raffaella R Comes Maria Colomba MC Bove Samantha S Didonna Vittorio V Diotaiuti Sergio S Giotta Francesco F Latorre Agnese A La Forgia Daniele D Nardone Annalisa A Pomarico Domenico D Ressa Cosmo Maurizio CM Rizzo Alessandro A Tamborra Pasquale P Zito Alfredo A Lorusso Vito V Fanizzi Annarita A

PloS one 20220919 9

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions ...[more]

PMID: 36121822

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Project description:Backgroundclinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this study was to identify novel and modifiable factors that have potential to improve diagnostic accuracy of ADL risk, with the long-term goal of guiding future interventions to minimize ADL disruption.Methodsstudy participants included 79 people with HIV (PWH; mean age = 63; range = 55-80) enrolled in neuroHIV studies at University California San Francisco (UCSF) between 2016 and 2019. All participants were virally suppressed and exhibited objective evidence of neurocognitive impairment. ADL status was defined as either normative (n = 39) or at risk (n = 40) based on a task-based protocol. Gradient boosted multivariate regression (GBM) was employed to identify the combination of variables that differentiated ADL subgroup classification. Predictor variables included demographic factors, HIV disease severity indices, brain white matter integrity quantified using diffusion tensor imaging, cognitive test performance, and health co-morbidities. Model performance was examined using average Area Under the Curve (AUC) with repeated five-fold cross validation.Findingsthe univariate GBM yielded an average AUC of 83% using Wide Range Achievement test 4 (WRAT-4) reading score, self-reported thought confusion and difficulty reading, radial diffusivity (RD) in the left external capsule, fractional anisotropy (FA) in the left cingulate gyrus, and Stroop performance. The model allowing for two-way interactions modestly improved classification performance (AUC of 88%) and revealed synergies between race, reading ability, cognitive performance, and neuroimaging metrics in the genu and uncinate fasciculus. Conversion of Neuropsychological Assessment Battery Daily Living Module (NAB-DLM) performance from raw scores into T scores amplified differences between White and non-White study participants.Interpretationdemographic and sociocultural factors are critical determinants of ADL risk status among older PWH who meet diagnostic criteria for neurocognitive impairment. Task-based ADL assessment that relies heavily on reading proficiency may artificially inflate the frequency/severity of ADL impairment among diverse clinical populations. Culturally relevant measures of ADL status are needed for individuals with acquired neurocognitive disorders, including HAND.

Dataset Information

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

Publications

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.

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