Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9

• URL: http://arxiv.org/abs/2408.16256v1
• Date: Thu, 29 Aug 2024 04:35:36 GMT
• Title: Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9
• Authors: Xia Jiang, Yijun Zhou, Alan Wells, Adam Brufsky,
• Abstract summary: Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. Current prognostic metrics are of little actionable value for 4 of the 5 women seemingly cured after local treatment. We develop algorithms that present a AUC of up to 0.9 in ROC analyses, using only extant data.
• Score: 6.712251433139411
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. That there are over 300,000 breast cancers newly diagnosed each year suggests that only a fraction of the cancers result in mortality. Thus, most of the women undergo seemingly curative treatment for localized cancers, but a significant later succumb to metastatic disease for which current treatments are only temporizing for the vast majority. The current prognostic metrics are of little actionable value for 4 of the 5 women seemingly cured after local treatment, and many women are exposed to morbid and even mortal adjuvant therapies unnecessarily, with these adjuvant therapies reducing metastatic recurrence by only a third. Thus, there is a need for better prognostics to target aggressive treatment at those who are likely to relapse and spare those who were actually cured. While there is a plethora of molecular and tumor-marker assays in use and under-development to detect recurrence early, these are time consuming, expensive and still often un-validated as to actionable prognostic utility. A different approach would use large data techniques to determine clinical and histopathological parameters that would provide accurate prognostics using existing data. Herein, we report on machine learning, together with grid search and Bayesian Networks to develop algorithms that present a AUC of up to 0.9 in ROC analyses, using only extant data. Such algorithms could be rapidly translated to clinical management as they do not require testing beyond routine tumor evaluations.

Related papers

• Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities [61.36288157482697]
  In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction.
  arXiv  Detail & Related papers  (2024-11-07T12:48:27Z)
• Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer [71.91773485443125]
  Transfer learning is a technique that leverages acquired features from a domain with richer data to enhance the performance of a domain with limited data. In this paper, we investigate the improvement of clinically significant prostate cancer prediction in T2-weighted images through transfer learning from breast cancer.
  arXiv  Detail & Related papers  (2024-05-13T15:57:27Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening [15.6839495538166]
  The overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. This study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode.
  arXiv  Detail & Related papers  (2024-01-26T07:58:09Z)
• Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR [1.29410702835589]
  Specific medical cancer screening methods are often costly, time-consuming, and weakly applicable on a large scale. This paper presents a novel Can-SAVE cancer risk assessment method combining a survival analysis approach with a gradient-boosting algorithm. It is highly accessible and resource-efficient, utilizing only a sequence of high-level medical events.
  arXiv  Detail & Related papers  (2023-09-26T16:15:54Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• An Artificial Intelligence Outlook for Colorectal Cancer Screening [1.417373050337415]
  Colorectal cancer is the third most common tumor in men and the second in women, accounting for 10% of all tumors worldwide. It ranks second in cancer-related deaths with 9.4%, following lung cancer.
  arXiv  Detail & Related papers  (2022-09-05T07:27:50Z)
• A Combined PCA-MLP Network for Early Breast Cancer Detection [0.0]
  We have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Our 4 layers-PCA network has obtained the best accuracy of 100% with a mean of 90.48% on the BCCD dataset.
  arXiv  Detail & Related papers  (2022-06-18T06:17:40Z)
• Breast cancer detection using artificial intelligence techniques: A systematic literature review [3.6608644315416585]
  In 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Deep learning has been designed to analyze the most important features affecting detection and treatment of serious diseases.
  arXiv  Detail & Related papers  (2022-03-08T13:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.