Predictive factors of an axillary pathological complete response of node-positive breast cancer to neoadjuvant chemotherapy

Iwamoto, Naoko; Aruga, Tomoyuki; Horiguchi, Shinichiro; Saita, Chiaki; Onishi, Mai; Goto, Risa; Ishiba, Toshiyuki; Honda, Yayoi; Miyamoto, Hiromi; Kuroi, Katsumasa

doi:10.1007/s00595-019-01858-x

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery. Many studies [104,106,107] have similar results, indicating the importance of MRI complete response in primary lesion rate is an important independent for predicting of axillary pCR.…”

Section: Lymph Node Response Evaluationmentioning

confidence: 65%

“…But the post-NAT MRI obtained a high negative predictive value (94% and 97.3%, respectively), which means that negative post-NAT MRI can accurately exclude the axillary lymph node diseases [101,102]. Before and during NAT, lymph node pCR can be predicted by negative hormone receptor and positive HER2 receptor status [103][104][105], lower clinical T and N stage [103], higher histological/nuclear grade [103], and treatment response to NAT of breast primary lesions [106,107]. According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery.…”

Section: Lymph Node Response Evaluationmentioning

confidence: 99%

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The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

Guo

Yang

et al. 2020

Chin J Acad Radiol

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Breast magnetic resonance imaging (MRI) is always considered to be more accurate for evaluating neoadjuvant therapy (NAT) response than mammography, ultrasound and clinical examination. Preoperative MRI aims to evaluate residual disease to facilitate surgical planning. MRI scans performed before and/or during NAT aim to predict the treatment response and thus adjust the NAT plan at an early stage. MRI accuracy depends on tumor morphology, histology, shrinkage pattern and molecular subtype. Combining multiparameter functional MRI can improve the prediction accuracy. In addition, radiomics offers high-dimensional and high-throughput signatures and were shown to predict treatment response before NAT. This article reviews the indicators of breast MRI for evaluating NAT response, including morphological MRI features, enhancement and pharmacokinetic parameters, apparent diffusion coefficient (ADC), choline concentration, etc., even radiomic signatures and illustrates their advantages, limitations and future directions.

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Section: Lymph Node Response Evaluationmentioning

confidence: 65%

Section: Lymph Node Response Evaluationmentioning

confidence: 99%

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

Guo

Yang

et al. 2020

Chin J Acad Radiol

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“…3 In this study, we presented We considered using the DLR scores from both tasks and the clinical characteristics as initial features for model development because some previous studies reported that there may be some correlation between the clinical responses of the primary tumor and ALN to NAC. [20][21][22] However, the scores generated from DLR-LNM and DLR-PCR were not selected as key features in the DLRN-PCR and DLRN-LNM models, respectively. Based on this result, we can conclude that there is no significant association between the scores generated from the DLR-PCR and DLR-LNM models, and the status of the tumor and ALN after NAC are relatively independent.…”

Section: Discussionmentioning

confidence: 99%

“…DLRN was developed based on the DLR scores from both tasks and the clinical characteristics because the DLR model combined with clinical characteristics may further improve the prediction performance and there may be some correlation between the two prediction tasks in this study. [20][21][22] The detailed methods of DLRN model construction and the stepwise multivariate logistic regression analysis are provided in the supporting information S1 and Table S2.…”

Section: Dlrn Model Constructionmentioning

confidence: 99%

“…Deep learning scores of both tumor PCR and LNM status, and the clinical characteristics were included in the model development for prediction of tumor PCR and LNM status because there may be a certain correlation between the status of tumor and the status of LN after NAC. [20][21][22] The performance of our DLRN models were verified on a retrospective multicenter data set with 484 patients. To the best of our knowledge, this is the first study to determine the feasibility of DLRN based on multitime-point US images (pre-NAC and after-NAC) for predicting the status of tumor and LNM in BC patients.…”

Section: Introductionmentioning

confidence: 98%

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Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study

Tong

et al. 2022

Cancer

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Abatract Background Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC. Methods In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort; H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR‐PCR) and the LNM status (DLR‐LNM) after NAC based on pre‐NAC and after‐NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN‐PCR and DLRN‐LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR‐PCR and DLR‐LNM. Results In the validation and test cohorts, DLRN‐PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN‐LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes. Conclusions The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC. Plain language summary In this study, we proposed two deep learning radiomics nomogram models based on pre‐neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of the tumor and ALN separately. Our model can potentially provide a noninvasive and personalized method to offer decision support for organ preservation and avoidance of excessive surgery.

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Breast cancer subtypes affect the ultrasound performance for axillary lymph node status evaluation after neoadjuvant chemotherapy: a retrospective analysis

Fei

Wang

Meng

et al. 2021

Japanese Journal of Clinical Oncology

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Purpose The aim of our study was to investigate the effect of breast cancer subtypes on the diagnostic value of axillary ultrasound for node status evaluation after neoadjuvant chemotherapy. Patients and methods Pathologic node-positive breast cancer patients underwent axillary ultrasound imaging after neoadjuvant chemotherapy were retrospectively reviewed. The enrolled patients were classified into four subtypes: Luminal A, Luminal B, human epidermal growth factor receptor 2-enriched and triple-negative. Ultrasound images of axillary nodes were reviewed and were evaluated as normal or abnormal and were associated with final pathologic results. Diagnostic value of axillary ultrasound was assessed in four subtypes based on sensitivity, specificity, positive predictive value and negative predictive value. The diagnostic value of axillary ultrasound as well as clinical and pathological characteristics was compared between four breast cancer subtypes using chi-square test or fisher’s exact test. Result Luminal A subtype had highest positive predictive value (92.1%), lowest sensitivity (43.8%) and lowest negative predictive value (11.8%). Triple-negative subtype had lowest positive predictive value (73.2%), highest sensitivity (76.9%) and highest negative predictive value (59.1%) (P < 0.05). Luminal B and human epidermal growth factor receptor 2-enriched subtypes had medium sensitivity, positive predictive value and negative predictive value. Conclusion The diagnostic value of axillary ultrasound for node residue disease assessment after neoadjuvant chemotherapy is different between four breast cancer subtypes.

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Predictive factors of an axillary pathological complete response of node-positive breast cancer to neoadjuvant chemotherapy

Cited by 5 publications

References 19 publications

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study

Breast cancer subtypes affect the ultrasound performance for axillary lymph node status evaluation after neoadjuvant chemotherapy: a retrospective analysis

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