The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer

Qu, Jinrong; Shen, Chen; Qin, Jianjun; Wang, Zhaoqi; Liu, Zhenyu; Guo, Jia; Zhang, Hongkai; Gao, Pengrui; Bei, Tianxia; Wang, Yingshu; Liu, Hui; Kamel, Ihab R.; Tian, Jie; Li, Hailiang

doi:10.1007/s00330-018-5583-z

Cited by 68 publications

(51 citation statements)

References 26 publications

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“…Recent studies have shown the value of radiomics features from primary lesion in predicting the lymph node metastasis for different cancer sites, e.g., CT radiomics features in colorectal cancer (33), MRI/CT radiomics features in bladder cancer (34,35) and esophageal cancer (36). For breast cancer, two recent studies have assessed the value of radiomics features extracted from the primary tumor region at DCE-MRI and diffusion-weighted MRI (DWI) in predicting sentinel lymph node metastasis, where the reported AUC, sensitivity and specificity ranging from 0.805 to 0.869, 0.700-0.778, and 0.747-861 respectively (9,10).…”

Section: P-values Were Calculated By Using χ 2 Test or Wilcoxon Rank-mentioning

confidence: 99%

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

et al. 2020

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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. Sun et al. Ultrasound-CNN Predicted Breast Cancer MetastasisConclusions: 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.

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Section: P-values Were Calculated By Using χ 2 Test or Wilcoxon Rank-mentioning

confidence: 99%

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

et al. 2020

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“…Previous studies have shown that quantitative radiomics features can provide insight into personalized medicine and potentially improve diagnostic, prognostic, and predictive accuracy [12]. Radiomics signatures, which consist of radiomic features, have been associated with clinical prognosis across a wide range of cancer types and are conveniently used to facilitate the preoperative individualized prediction of LNM [13][14][15]. However, there are only a few studies investigating the association of US-based radiomic features with LNM in patients with PTC, and fewer or no studies specifically focusing on lateral LNM [16,17].…”

Section: Introductionmentioning

confidence: 99%

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

et al. 2020

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Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). Methods Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). Results The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649-0.770) in the training cohort and was 0.621 (95% CI: 0.560-0.682) in the validation cohort. Conclusions The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was

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“…Our finding may also support that using a combination of signatures covering different aspects could be a promising approach to help improve precision medicine. Comparing with previous studies of handcrafted radiomics model (9,27,35), CV-and deep-radiomics features were added as independent signatures in our work, which significantly improved the model's predictive ability for LN metastasis of ESCC (C-statistic, 0.840, in external validation cohort).…”

Section: Discussionmentioning

confidence: 78%

“…Clinical determination of LN metastasis according to LN size criteria on preoperative CT is limited. Recently, radiomics, as an emerging tool, has shown potential values in predicting LN metastasis by extracting high-throughput quantitative features from medical images (8)(9)(10). However, most of the features extracted are defined by mathematical formulas (also called handcrafted feature), which are shallow, susceptible to noise, and low-order image features.…”

Section: Introductionmentioning

confidence: 99%

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study

Yang

Cao

et al. 2020

Front. Oncol.

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Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Wu et al. Radiomics Approach Predicting ESCC LNM Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.

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The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer

Abstract: • The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features.

Cited by 68 publications

References 26 publications

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study

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