Early prediction of metastasis in women with locally advanced breast cancer

Rabinovici-Cohen, Simona; Tlusty, Tsvi; Rejo, Beatriz Grandal

doi:10.1117/12.2613169

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Before open sourcing it, we used fuse internally in multiple research projects (Raboh, Levanony, et al, 2022), (Rabinovici-Cohen, Tlusty, et al, 2022, (Jubran et al, 2021), (Tlusty et al, 2021), (Golts et al, 2022), (Barros et al) and experienced significant improvement in development time, reusability and collaboration. We were also able to meaningfully measure our progress and statistical significance of our results with off-the-shelf fuse.eval components that facilitate metrics' confidence interval calculation and model comparison.…”

Section: Statement Of Needmentioning

confidence: 99%

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Golts¹,

Raboh²,

Shoshan³

et al. 2023

JOSS

Self Cite

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Machine Learning is at the forefront of scientific progress in Healthcare and Medicine. To accelerate scientific discovery, it is important to have tools that allow progress iterations to be collaborative, reproducible, reusable and easily built upon without "reinventing the wheel" for each task. FuseMedML, or fuse, is a Python framework designed for accelerated Machine Learning (ML) based discovery in the medical domain. It is highly flexible and designed for easy collaboration, encouraging code reuse. Flexibility is enabled by a generic data object design where data is kept in a nested (hierarchical) Python dictionary (NDict), allowing to efficiently process and fuse information from multiple modalities. Functional components allow to specify input and output keys, to be read from and written to the nested dictionary. Easy code reuse is enabled through key components implemented as standalone packages under the main fuse repo using the same design principles. These include fuse.data -a flexible data processing pipeline, fuse.dl -reusable Deep Learning (DL) model architecture components and loss functions, and fuse.eval -a library for evaluating ML models.

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Section: Statement Of Needmentioning

confidence: 99%

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Golts¹,

Raboh²,

Shoshan³

et al. 2023

JOSS

Self Cite

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“…There have been some approaches to utilizing MRI images to predict the recurrence of cancer. For example, tumor volume approximation [ 15 , 16 ] and texture features have been shown to have a clear connection to recurrence [ 17 , 18 ]. In another study [ 19 ], both morphological (rim enhancement, etc.)…”

Section: Related Workmentioning

confidence: 99%

“…In practice, any of these designed features can be visualized for the clinical operator with MRI volume if necessary. Finally, the volumetric features were augmented with clinical metadata and transformed via 5 fully connected layers with (16,16,16,32,32) filters to 32 features.…”

Section: Dixon-adc Componentmentioning

confidence: 99%

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

Rabinovici-Cohen

Rejo

Hexter

et al. 2022

Cancers

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In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.

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Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers

Rabinovici-Cohen,

Platt,

Iwamori

et al. 2024

Preprint

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Chronic kidney disease (CKD) is a complex condition where the kid- neys are damaged and progressively lose their ability to filter blood, 10% of the world population have the disease that often goes undetected un- til it is too late for intervention. Using the UK Biobank (UKBB) we constructed a CKD cohort of patients (n=46,986) with genomic, clinical and demographic data available, a subset (n=2,151) having also whole body Magnetic Resonance Imaging (MRI) scans. We used this multi- modal cohort to successfully predict, from initially healthy patients, their 5-year outcomes for End-Stage Renal Disease (ESRD, n=210, AUC=0.804 +/- 0.03 with 5 fold cross-validation) and the larger cohort for validation to predict time-to ESRD and perform Genome-wide association studies (GWAS). Extracting important clinical, phenotypic and genetic features from the models, we were able to stratify the cohorts based on a novel set of significant previously unreported SNPs related to mitochondria/cell death, kidney development and function. In particular, we show that the risk allele of SNP rs1383063 present in 30% of the population irrespec- tive of ancestry and putatively regulating MAGI-1, a gene expressed in the podocyte slit diaphragm, is a strong predictor of ESRD and stratifies male populations of older age.

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Early prediction of metastasis in women with locally advanced breast cancer

Cited by 3 publications

References 25 publications

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

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

Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers

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