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Dell'Aquila K, Vadlamani A, Maldjian T, Fineberg S, Eligulashvili A, Chung J, Adam R, Hodges L, Hou W, Makower D, Duong TQ. Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population. Breast Cancer Res 2024; 26:7. [PMID: 38200586 PMCID: PMC10782738 DOI: 10.1186/s13058-023-01762-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population. METHODS Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation. RESULTS pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). CONCLUSION Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.
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Affiliation(s)
- Kevin Dell'Aquila
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Abhinav Vadlamani
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Takouhie Maldjian
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Susan Fineberg
- Department of Pathology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Anna Eligulashvili
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Julie Chung
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Richard Adam
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Laura Hodges
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Wei Hou
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
| | - Della Makower
- Department of Oncology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
- Center for Health Data Innovation, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, USA.
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Adam R, Dell'Aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Res 2023; 25:87. [PMID: 37488621 PMCID: PMC10367400 DOI: 10.1186/s13058-023-01687-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023] Open
Abstract
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.
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Affiliation(s)
- Richard Adam
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Kevin Dell'Aquila
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Laura Hodges
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Takouhie Maldjian
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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Khan N, Adam R, Huang P, Maldjian T, Duong TQ. Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review. Tomography 2022; 8:2784-2795. [PMID: 36412691 PMCID: PMC9680498 DOI: 10.3390/tomography8060232] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.
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Adam R, Duong T, Hodges L, Lu J, Maldjian T. Internal mammary lymph nodal response to neoadjuvant chemotherapy on imaging and breast cancer prognosis. Ann Med Surg (Lond) 2022; 84:104900. [DOI: 10.1016/j.amsu.2022.104900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/06/2022] [Accepted: 11/06/2022] [Indexed: 11/16/2022] Open
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Adam R, Duong T, Hodges L, Staeger-Hirsch C, Maldjian T. Mammographic findings of diffuse axillary tail trabecular thickening following immunization with mRNA COVID-19 vaccines: Case series study. Radiol Case Rep 2022; 17:2841-2849. [PMID: 35702669 PMCID: PMC9186537 DOI: 10.1016/j.radcr.2022.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 11/25/2022] Open
Abstract
Axillary lymphadenopathy has been reported after ipsilateral COVID-19 vaccination and can cause confusion for possible malignancy [1]. Intrinsic findings isolated to the breast has not been previously reported. This is the first case series of ipsilateral reversible changes of diffuse axillary tail trabecular thickening on screening mammography in totally asymptomatic patients in connection with COVID vaccination, 3 of which were isolated findings, confirmed by complete resolution of all imaging findings on follow up. In all instances, imaging was performed within 1 week of the first or third dose of an mRNA COVID-19 vaccine. These findings can be confused with breast cancer. Spontaneous resolution distinguishes vaccine-related findings from breast cancer.
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