1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis.

          Methods

          In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set ( n = 542) and independent test set ( n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups.

          Findings

          In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6–100) and NSLNs (sensitivity=98.4%, 95% CI 95.6–99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2–100) and 91.7% (95% CI 88.8–97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment.

          Interpretation

          The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer.

          Funding

          The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.

          Related collections

          Most cited references 35

          • Record: found
          • Abstract: found
          • Article: not found

          A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer.

          Although numerous studies have shown that the status of the sentinel node is an accurate predictor of the status of the axillary nodes in breast cancer, the efficacy and safety of sentinel-node biopsy require validation. From March 1998 to December 1999, we randomly assigned 516 patients with primary breast cancer in whom the tumor was less than or equal to 2 cm in diameter either to sentinel-node biopsy and total axillary dissection (the axillary-dissection group) or to sentinel-node biopsy followed by axillary dissection only if the sentinel node contained metastases (the sentinel-node group). The number of sentinel nodes found was the same in the two groups. A sentinel node was positive in 83 of the 257 patients in the axillary-dissection group (32.3 percent), and in 92 of the 259 patients in the sentinel-node group (35.5 percent). In the axillary-dissection group, the overall accuracy of the sentinel-node status was 96.9 percent, the sensitivity 91.2 percent, and the specificity 100 percent. There was less pain and better arm mobility in the patients who underwent sentinel-node biopsy only than in those who also underwent axillary dissection. There were 15 events associated with breast cancer in the axillary-dissection group and 10 such events in the sentinel-node group. Among the 167 patients who did not undergo axillary dissection, there were no cases of overt axillary metastasis during follow-up. Sentinel-node biopsy is a safe and accurate method of screening the axillary nodes for metastasis in women with a small breast cancer. Copyright 2003 Massachusetts Medical Society
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy.

            The standard of care for breast cancer patients with sentinel lymph node (SLN) metastases includes complete axillary lymph node dissection (ALND). However, many question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the axilla could assist greatly in decision-making regarding further treatment. Pathological features of the primary tumor and SLN metastases of 702 patients who underwent complete ALND were assessed with multivariable logistic regression to predict the presence of additional disease in the non-SLNs of these patients. A nomogram was created using pathological size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. The model was subsequently applied prospectively to 373 patients. The nomogram for the retrospective population was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the prospective group, the model accurately predicted likelihood of non-SLN disease (ROC, 0.77). We have developed a user-friendly nomogram that uses information commonly available to the surgeon to easily and accurately calculate the likelihood of having additional, non-SLN metastases for an individual patient.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Locoregional Recurrence After Sentinel Lymph Node Dissection With or Without Axillary Dissection in Patients With Sentinel Lymph Node Metastases: Long-term Follow-up From the American College of Surgeons Oncology Group (Alliance) ACOSOG Z0011 Randomized Trial.

              The early results of the American College of Surgeons Oncology Group (ACOSOG) Z0011 trial demonstrated no difference in locoregional recurrence for patients with positive sentinel lymph nodes (SLNs) randomized either to axillary lymph node dissection (ALND) or sentinel lymph node dissection (SLND) alone. We now report long-term locoregional recurrence results.
                Bookmark

                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                24 September 2020
                October 2020
                24 September 2020
                : 60
                Affiliations
                [a ]Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
                [b ]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
                [c ]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
                [d ]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China
                [e ]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
                [f ]Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China
                [g ]Department of general surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
                [h ]Department of MRI Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
                Author notes
                [* ]Correspondence to: Jiawei Tian, MD, Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nan Gang Dist., Harbin, Heilongjiang, China. jwtian2004@ 123456163.com
                [** ]Corresponding author at: Jie Tian, PhD, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East road, Beijing, China. jie.tian@ 123456ia.ac.cn
                [1]

                These authors contributed equally to this work as co-first authors.

                Article
                S2352-3964(20)30394-7 103018
                10.1016/j.ebiom.2020.103018
                7519251
                32980697
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                Categories
                Research paper

                Comments

                Comment on this article