Multi-modal AI for comprehensive breast cancer prognostication

• URL: http://arxiv.org/abs/2410.21256v1
• Date: Mon, 28 Oct 2024 17:54:29 GMT
• Title: Multi-modal AI for comprehensive breast cancer prognostication
• Authors: Jan Witowski, Ken Zeng, Joseph Cappadona, Jailan Elayoubi, Elena Diana Chiru, Nancy Chan, Young-Joon Kang, Frederick Howard, Irina Ostrovnaya, Carlos Fernandez-Granda, Freya Schnabel, Ugur Ozerdem, Kangning Liu, Zoe Steinsnyder, Nitya Thakore, Mohammad Sadic, Frank Yeung, Elisa Liu, Theodore Hill, Benjamin Swett, Danielle Rigau, Andrew Clayburn, Valerie Speirs, Marcus Vetter, Lina Sojak, Simone Muenst Soysal, Daniel Baumhoer, Khalil Choucair, Yu Zong, Lina Daoud, Anas Saad, Waleed Abdulsattar, Rafic Beydoun, Jia-Wern Pan, Haslina Makmur, Soo-Hwang Teo, Linda Ma Pak, Victor Angel, Dovile Zilenaite-Petrulaitiene, Arvydas Laurinavicius, Natalie Klar, Brian D. Piening, Carlo Bifulco, Sun-Young Jun, Jae Pak Yi, Su Hyun Lim, Adam Brufsky, Francisco J. Esteva, Lajos Pusztai, Yann LeCun, Krzysztof J. Geras,
• Abstract summary: We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts. Results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.
• Score: 18.691704371847855
• License:
• Abstract: Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.

Related papers

• Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions [0.8864540224289991]
  Pathologists are facing an increasing workload due to a growing volume of cases and the need for more comprehensive diagnoses. We developed an artificial intelligence (AI) model for triaging cutaneous melanocytic lesions based on whole slide images.
  arXiv  Detail & Related papers  (2024-10-14T13:49:04Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Leveraging cough sounds to optimize chest x-ray usage in low-resource settings [35.63955045151504]
  We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray.
  arXiv  Detail & Related papers  (2024-02-13T20:54:55Z)
• Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features [0.6679306163028237]
  The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict associated breast cancer recurrence risks. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation.
  arXiv  Detail & Related papers  (2024-01-28T23:33:56Z)
• Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study [1.2397589403129072]
  AI has proven to be helpful for enhancing melanoma detection. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes. We assessed 'All Data are Ext' (ADAE), an established open-source algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists.
  arXiv  Detail & Related papers  (2024-01-25T14:03:54Z)
• Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans [38.46196471197819]
  Gastric cancer is the third leading cause of cancer-related mortality worldwide. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner.
  arXiv  Detail & Related papers  (2023-07-10T12:49:36Z)
• A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles [0.0]
  The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.
  arXiv  Detail & Related papers  (2023-03-13T10:08:13Z)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans [36.093580055848186]
  Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
  arXiv  Detail & Related papers  (2021-10-17T04:37:24Z)
• Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm [0.0]
  Pancreatic cancer is a lethal disease, hard to diagnose and results in poor prognosis and high mortality. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT.
  arXiv  Detail & Related papers  (2020-04-03T06:06:43Z)

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.