Dataset Information

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

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.

SUBMITTER: Guo X

PROVIDER: S-EPMC7519251 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

Guo Xu X Liu Zhenyu Z Sun Caixia C Zhang Lei L Wang Ying Y Li Ziyao Z Shi Jiaxin J Wu Tong T Cui Hao H Zhang Jing J Tian Jie J Tian Jiawei J

EBioMedicine 20200924

<h4>Background</h4>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.<h4>Methods</h4>In tot ...[more]

PMID: 32980697

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Project description:Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models. Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Project description:PurposeSentinel lymph node biopsy (SLNB) alone following neoadjuvant chemotherapy (NAC) remains controversial in patients with breast cancer who are initially lymph node-positive. The present study aimed to evaluate the impact of SLNB and axillary lymph node dissection (ALND) on breast cancer recurrence and survival in patients who converted from lymph node-positive to pathological node-negative (ypN0) after NAC.MethodsThis single-center retrospective study included 223 patients who converted to axillary lymph node-negative status after NAC and underwent breast and axillary surgery between January 2006 and December 2015. This study compared the overall survival (OS), disease-free survival (DFS), ipsilateral axillary lymph node recurrence rates and incidence of postoperative complications, especially, arm lymphedema and shoulder stiffness between SLNB and ALND.ResultsThis study included 223 patients with axillary pathological complete response (pCR) after NAC and surgery. The SLNB and ALND groups included 94 and 129 patients, respectively. The median follow-up time was 57 (range, 6-155) in the SLNB group and 99 (range 2-159) months in the ALND group. The corresponding 5-year OS and DFS rates were 96.3% and 94.2% (p = 0.392), and 89.2% and 86.4% (p = 0.671), respectively. Four patients (4.3%) in the SLNB group and nine (7.0%) in the ALND group developed locoregional recurrences. Ipsilateral axillary lymph node recurrence and distant metastasis were observed in one (1.1%) and three (2.3%) patients, and in 10 (10.6%) and 11 (8.5%) patients, respectively. Patients in the ALND group were more likely than their SLNB counterparts to experience complications, such as shoulder stiffness (9 [7.0%] vs. 4 [4.3%] patients, p = 0.57). The rate of lymphedema in the ALND group was three times that in the SLNB group (35 [27.1%] vs. 8 [8.5%] patients, p < 0.001).ConclusionAs an alternative to ALND, SLNB has oncological safety in patients with axillary pathological complete response after NAC.

Project description:Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Project description:PurposeTo develop a clinical-radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases.Materials and methodsThe MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical-radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC).ResultsThe clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical-radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR.ConclusionWe developed a clinical-radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

Project description:BackgroundSentinel node biopsy (SNB) is an important step in melanoma staging and prognostication. It is commonly performed for patients with intermediate thickness melanomas, based on clinicopathological features. However, only 20-25% of patients eventually demonstrate nodal involvement. The aim of this study was to evaluate whether tissue biomarkers with links to melanoma biology, together with clinicopathological parameters, could aid in the prediction of sentinel node involvement and improve selection of patients for SNB. In addition, we examined the role of these clinical or biological markers in disease outcome.MethodsWe collected a case-control cohort of 140 intermediate thickness (Breslow 0,9-4,0mm) melanoma patients with or without SNB involvement matched for age, gender, Breslow thickness and location. From this cohort, we tested the predictive value of common clinicopathological parameters (ulceration, mitotic count and tumor regression) and FMNL-2, ezrin and BRAF V600E immunoreactivity, for sentinel node involvement and survival. We further analyzed the correlations in the superficial spreading melanoma subtype.ResultsBased on our case control analysis, of the markers, BRAF V600E status (p = 0.010) and mitotic count (p = 0.036) correlated with SNB involvement. SNB status was a strong independent prognosticator for recurrence free survival (RFS p<0.001), melanoma specific survival (MSS p = 0.000) and overall survival (OS p = 0.029). In the superficially spreading melanoma subgroup, BRAF V600E positivity indicated poorer RFS (p = 0.039) and OS (p = 0.012). By combining the Breslow thickness, mitotic count and BRAF immunohistochemistry, we identified a group of superficially spreading melanomas with an excellent survival probability independent of SNB status.ConclusionsThese results demonstrate that BRAF immunohistochemistry could serve as a useful addition to a marker panel for selecting intermediate thickness melanoma patients for SNB.

Dataset Information

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

Publications

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

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