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Ameri MA, Shanbhag NM, Bin Sumaida A, Ansari J, Trad DA, Dawoud EA, Balaraj K. Oncotype DX in Breast Cancer Management: Insights and Outcomes From the United Arab Emirates. Cureus 2024; 16:e56535. [PMID: 38516286 PMCID: PMC10955450 DOI: 10.7759/cureus.56535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/23/2024] Open
Abstract
Introduction Breast cancer remains the most significant cancer affecting women worldwide, with an increasing incidence, especially in developing regions. The introduction of genomic tests like Oncotype DX has revolutionized personalized treatment, allowing for more tailored approaches to therapy. This study focuses on the United Arab Emirates (UAE), where breast cancer is the leading cause of cancer-related deaths among women, aiming to assess the predictive accuracy of the Oncotype DX test in categorizing patients based on recurrence risk. Materials and methods A retrospective cohort study was conducted on 95 breast cancer patients diagnosed at Tawam Hospital between 2013 and 2017 who underwent Oncotype DX testing. Data on patient demographics, tumor characteristics, treatment details, and Oncotype DX scores were collected. Survival analysis was performed using the Kaplan-Meier method, with the chi-square goodness of fit test assessing the model's adequacy. Results The cohort's age range was 27-71 years, with a mean age of 50, indicating a significant concentration of cases in the early post-menopausal period. The Oncotype DX analysis classified 55 patients (57.9%) as low risk, 29 (30.5%) as medium risk, and 11 (11.6%) as high risk of recurrence. The majority, 73 patients (76.8%), did not receive chemotherapy, highlighting the test's impact on treatment decisions. The survival analysis revealed no statistically significant difference in recurrence rates across the Oncotype DX risk categories (p = 0.268231). Conclusion The Oncotype DX test provides a valuable genomic approach to categorizing breast cancer patients by recurrence risk in the UAE. While the test influences treatment decisions, particularly the use of chemotherapy, this study did not find a significant correlation between Oncotype DX risk categories and actual recurrence events. These findings underscore the need for further research to optimize the use of genomic testing in the UAE's diverse patient population and enhance personalized treatment strategies in breast cancer management.
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Affiliation(s)
| | - Nandan M Shanbhag
- College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, ARE
- Oncology/Radiation Oncology, Tawam Hospital, Al Ain, ARE
- Oncology/Palliative Care, Tawam Hospital, Al Ain, ARE
| | | | | | | | | | - Khalid Balaraj
- Oncology/Radiation Oncology, Tawam Hospital, Al Ain, ARE
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Liu CJ, Zhang L, Sun Y, Geng L, Wang R, Shi KM, Wan JX. Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis. BMC Cancer 2023; 23:1134. [PMID: 37993845 PMCID: PMC10666295 DOI: 10.1186/s12885-023-11638-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients. METHODS We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included 'artificial intelligence', 'CT,' 'MRI', 'breast cancer' and 'lymph nodes'. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC). RESULTS A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79-0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66-0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0-4.8), a negative likelihood ratio of 0.18 (95% CI 0.13-0.26) and a diagnostic odds ratio of 25 (95% CI 17-38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79-0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69-0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7-7.0), a negative likelihood ratio of 0.15 (95% CI 0.08-0.27) and a diagnostic odds ratio of 30 (95% CI 12-72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82-0.88) and 0.91 (95% CI 0.88-0.93), respectively. CONCLUSION Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.
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Affiliation(s)
- Cheng-Jie Liu
- Department of Information Center, Lianyungang Human Resources and Social Security Bureau, Lianyungang, 222000, JiangSu, China
| | - Lei Zhang
- Department of Information System, Lianyungang 149 Hospital, Lianyungang, 222000, Jiangsu, China
| | - Yi Sun
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Lei Geng
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Rui Wang
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Kai-Min Shi
- Department of Information Center, Lianyungang Shuangcheng Information Technology Co., Ltd, Lianyungang, 222000, China
| | - Jin-Xin Wan
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China.
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Kay C, Martinez-Perez C, Dixon JM, Turnbull AK. The Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer. J Pers Med 2023; 13:1476. [PMID: 37888087 PMCID: PMC10608445 DOI: 10.3390/jpm13101476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/28/2023] Open
Abstract
The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.
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Affiliation(s)
- Charlene Kay
- Translational Oncology Research Group, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Carlos Martinez-Perez
- Translational Oncology Research Group, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - J Michael Dixon
- Edinburgh Breast Unit, Western General Hospital, NHS Lothian, Edinburgh Eh4 2XU, UK
| | - Arran K Turnbull
- Translational Oncology Research Group, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
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Maybank AK, Curtis H, Topp T, Barnes PJ. Metastatic Mammary Carcinoma Presenting as a Large Cystic Axillary Mass: A Report of an Unusual Case. Int J Surg Pathol 2023; 31:1409-1413. [PMID: 36803091 DOI: 10.1177/10668969231152574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
The differential diagnosis of cystic axillary masses is broad and includes intranodal lesions. Cystic metastatic tumor deposits are rare, and have been reported in a few tumor types, most commonly in the head and neck region, but rarely described with metastatic mammary carcinoma. We report a case of a 61-year-old female who presented with a large right axillary mass. Imaging studies revealed a cystic axillary mass and ipsilateral breast mass. She was managed with breast conservation surgery and axillary dissection for invasive ductal carcinoma, no special type, Nottingham grade 2 (21 mm). One of nine lymph nodes contained a cystic nodal deposit (52 mm), which resembled a benign inclusion cyst. Oncotype DX recurrence score for the primary tumor was low (8), conferring a low risk of disease recurrence despite the large size of the nodal metastatic deposit. A cystic pattern of metastatic mammary carcinoma is rare and important to recognize for accurate staging and management decisions.
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Affiliation(s)
- Allison K Maybank
- Department of Pathology, Nova Scotia Health and Dalhousie University, Halifax, NS, Canada
| | - Heather Curtis
- Department of Diagnostic Radiology, Nova Scotia Health and Dalhousie University, Halifax, NS, Canada
| | - Trevor Topp
- Department of Surgery, Nova Scotia Health and Dalhousie University, Halifax, NS, Canada
| | - Penny J Barnes
- Department of Pathology, Nova Scotia Health and Dalhousie University, Halifax, NS, Canada
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Nair NS, Kothari B, Gupta S, Kanann S, Vanmali V, Hawaldar R, Tondare A, Siddique S, Parmar V, Joshi S, Badwe RA. Validation of PREDICT Version 2.2 in a Retrospective Cohort of Indian Women With Operable Breast Cancer. JCO Glob Oncol 2023; 9:e2300114. [PMID: 38085062 PMCID: PMC10846767 DOI: 10.1200/go.23.00114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE Online prediction models that use known prognostic factors in breast cancer (BC) are routinely used to assist in decisions for adjuvant therapy. PREDICT Version 2.2 (P2.2) is one such online tool, which uses tumor size, lymph node involvement, grade, age, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, and Ki67. We performed an external validation in a retrospective cohort of patients treated at a tertiary center in India. METHODS Women with operable BC between 2008 and 2016 with nonmetastatic, T1-T2 invasive, and HER2 receptor-negative BC and with available 5-year overall survival (OS) data were selected. Median predicted 5-year OS rates were used to calculate predicted events for the whole cohort and subgroups. The chi-square test was used to evaluate the goodness of fit of the tool. RESULTS Of 11,760 cases registered between 2008 and 2016, 2,783 (23.66%) eligible patients with a median age of 50 (26-70) years and a median pT size of 2.5 (0.1-5) cm, 2,037 (73.19%) with grade 3 tumors, 1,172 (42.11%) with node-positive disease, 817 (29.35%) with triple-negative breast cancer, and 1,966 (70.64%) with HR-positive BC were included in the analysis. The observed 5-year OS and predicted 5-year OS in the whole cohort were 94.8% and 90.00%, respectively, with an absolute difference of 4.8% (95% CI, 3.417 to 6.198, P < .001). The observed 5-year OS and predicted 5-year OS were also different in various subgroups. CONCLUSION PREDICT version 2.2 overestimated the number of deaths, with lower predicted 5-year OS compared with the observed value, in this retrospective Indian cohort. The reasons for this discrepancy could be differing biologic characteristics and possible selection bias in our cohort. We recommend a prospective validation of PREDICT in Indian patients and advocate caution in its use until such validation is achieved.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - RA Badwe
- Tata Memorial Centre, Mumbai, India
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Challa B, Tahir M, Hu Y, Kellough D, Lujan G, Sun S, Parwani AV, Li Z. Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow. Mod Pathol 2023; 36:100216. [PMID: 37178923 DOI: 10.1016/j.modpat.2023.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/03/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.
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Affiliation(s)
- Bindu Challa
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Maryam Tahir
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Yan Hu
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - David Kellough
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovani Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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Laury RJ, Gloyeske N, Mettman D, Wagner JL, Fan F. Intraoperative sentinel lymph node evaluation in patients with node-positive breast cancer status post neoadjuvant systemic therapy - An institutional experience. Ann Diagn Pathol 2022; 60:152012. [DOI: 10.1016/j.anndiagpath.2022.152012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/17/2022] [Indexed: 11/16/2022]
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