Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Xu, Feng; Zhu, Chuang; Tang, Wenqi; Wang, Ying; Li, Jie; Jiang, Hongchuan; Shi, Zhongyue; Li, Jun; Jin, Mulan

doi:10.1101/2021.10.10.21264721

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…At present, the use of cutting-edge DL technology to predict the preoperative LNM status of tumors, including gastric cancer (26,27), breast cancer (28), colorectal cancer (29), lung cancer (30), urothelial cancer (31) and EC (7,32), usually relies on the readily-available MRI/ CT and ultrasound imaging. Clinical experience has illustrated that the existing LNM risk stratification criteria, including the Mayo criteria and NCCN guidelines, will expose 75% of EC patients to unnecessary lymphadenectomy (33) and about 10% of low-risk and 15% of early-stage ECs will progress to LNM (34).…”

Section: Discussionmentioning

confidence: 99%

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Feng¹,

Zhao²,

Chen³

et al. 2023

Quant Imaging Med Surg

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Background: The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data.Methods: A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. Results:The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. Conclusions:The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

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Section: Discussionmentioning

confidence: 99%

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Feng¹,

Zhao²,

Chen³

et al. 2023

Quant Imaging Med Surg

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“…Some MRI features of primary breast tumor were closely associated with ALN metastases, such as irregular shapes, larger tumors, equal or slightly elevated T2WI signal, heterogeneous enhancement, and washout time intensity curve (TIC) (17). Xu et al (18) showed that primary tumor biopsy whole-slide images (WSIs) as a complementary imaging tool has the potential for ALN metastasis prediction. Zhou et al (19) demonstrated that a deep learning algorithm from US images of primary breast cancer has high accuracy in predicting ALN metastasis.…”

Section: Discussionmentioning

confidence: 99%

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Zeng¹,

Chen²,

Lin³

et al. 2022

Quant Imaging Med Surg

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Background: Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with earlystage breast cancer.Methods: Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test.Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.Results: This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).Conclusions: Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

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Gamma Corrected Pyramid Pix2pix – Breast Cancer HE to IHC Image Generation

Kulkarni,

Thota,

Nayak

et al. 2024

Intelligent Systems Design and Applications

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Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Cited by 3 publications

References 44 publications

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Gamma Corrected Pyramid Pix2pix – Breast Cancer HE to IHC Image Generation

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