Robert Browne, Peter McAnena, Niamh O'Halloran, Brian M Moloney, Emily Crilly, Michael J Kerin, Aoife J Lowery
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Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.</p><p><strong>Results: </strong>The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. Positive predictive value was the highest in nonluminal versus Luminal A disease (45.0% vs 25.0%, <i>P</i> < .001), with higher rates of false positivity in nonluminal subtypes (<i>P</i> = .002). Tumour grade, T category, and histological subtype were all independent predictors of MRI accuracy regarding post-NAC tumour size.</p><p><strong>Conclusion: </strong>Magnetic resonance imaging alone is insufficient to accurately predict pCR in breast cancer patients post-NAC. Magnetic resonance imaging predictions of pCR are more accurate in nonluminal subtypes. Tumour grade, T category, and histological subtype should be considered when evaluating post-NAC tumour sizes.</p>","PeriodicalId":9163,"journal":{"name":"Breast Cancer : Basic and Clinical Research","volume":" ","pages":"11782234221103504"},"PeriodicalIF":1.8000,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e4/94/10.1177_11782234221103504.PMC9234834.pdf","citationCount":"2","resultStr":"{\"title\":\"Preoperative Breast Magnetic Resonance Imaging as a Predictor of Response to Neoadjuvant Chemotherapy.\",\"authors\":\"Robert Browne, Peter McAnena, Niamh O'Halloran, Brian M Moloney, Emily Crilly, Michael J Kerin, Aoife J Lowery\",\"doi\":\"10.1177/11782234221103504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The ability to accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer would improve patient selection for specific treatment strategies, would provide important information for patients to aid in the treatment selection process, and could potentially avoid the need for more extensive surgery. The diagnostic performance of magnetic resonance imaging (MRI) in predicting pCR has previously been studied, with mixed results. Magnetic resonance imaging performance may also be influenced by tumour and patient factors.</p><p><strong>Methods: </strong>Eighty-seven breast cancer patients who underwent NAC were studied. Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.</p><p><strong>Results: </strong>The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. 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引用次数: 2
摘要
引言:准确预测乳腺癌新辅助化疗(NAC)后病理完全反应(pCR)的能力将改善患者对特定治疗策略的选择,为患者提供重要信息,帮助他们选择治疗过程,并有可能避免更广泛的手术。磁共振成像(MRI)在预测pCR中的诊断性能先前已经进行了研究,结果好坏参半。磁共振成像性能也可能受到肿瘤和患者因素的影响。方法:对87例行NAC的乳腺癌患者进行分析。比较nac术前和nac后的MRI表现和手术切除后的病理表现。评估患者和肿瘤特征对MRI准确性的影响。结果:参与者的平均(SD)年龄为48.7(10.3)岁。nac后MRI pCR阳性率为19.5%(19/87)。预测pCR的敏感性、特异性、阳性预测值(PPV)、阴性预测值和准确性分别为52.9%、77.1%、36.0%、87.1%和72.4%。非腔内A与腔内A疾病的阳性预测值最高(45.0% vs 25.0%, P P = 0.002)。肿瘤分级、T分类和组织学亚型都是nac后肿瘤大小的MRI准确性的独立预测因子。结论:单纯磁共振成像不足以准确预测nac后乳腺癌患者的pCR。pCR的磁共振成像预测在非腔型亚型中更为准确。在评估nac后肿瘤大小时,应考虑肿瘤分级、T分类和组织学亚型。
Preoperative Breast Magnetic Resonance Imaging as a Predictor of Response to Neoadjuvant Chemotherapy.
Introduction: The ability to accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer would improve patient selection for specific treatment strategies, would provide important information for patients to aid in the treatment selection process, and could potentially avoid the need for more extensive surgery. The diagnostic performance of magnetic resonance imaging (MRI) in predicting pCR has previously been studied, with mixed results. Magnetic resonance imaging performance may also be influenced by tumour and patient factors.
Methods: Eighty-seven breast cancer patients who underwent NAC were studied. Pre-NAC and post-NAC MRI findings were compared with pathologic findings postsurgical excision. The impact of patient and tumour characteristics on MRI accuracy was evaluated.
Results: The mean (SD) age of participants was 48.7 (10.3) years. The rate of pCR based on post-NAC MRI was 19.5% overall (19/87). The sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy in predicting pCR were 52.9%, 77.1%, 36.0%, 87.1%, and 72.4%, respectively. Positive predictive value was the highest in nonluminal versus Luminal A disease (45.0% vs 25.0%, P < .001), with higher rates of false positivity in nonluminal subtypes (P = .002). Tumour grade, T category, and histological subtype were all independent predictors of MRI accuracy regarding post-NAC tumour size.
Conclusion: Magnetic resonance imaging alone is insufficient to accurately predict pCR in breast cancer patients post-NAC. Magnetic resonance imaging predictions of pCR are more accurate in nonluminal subtypes. Tumour grade, T category, and histological subtype should be considered when evaluating post-NAC tumour sizes.
期刊介绍:
Breast Cancer: Basic and Clinical Research is an international, open access, peer-reviewed, journal which considers manuscripts on all areas of breast cancer research and treatment. We welcome original research, short notes, case studies and review articles related to breast cancer-related research. Specific areas of interest include, but are not limited to, breast cancer sub types, pathobiology, metastasis, genetics and epigenetics, mammary gland biology, breast cancer models, prevention, detection, therapy and clinical interventions, and epidemiology and population genetics.