Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

Yang, Jing; Wu, Qingyao; Xu, Lei; Wang, Zijie; Su, Kefan; Liu, Ruiqing; Yen, Eric; Liu, Shunli; Qin, Jiale; Rong, Yi; Lu, Yun; Niu, Tianye

doi:10.1016/j.radonc.2020.06.004

Cited by 42 publications

(49 citation statements)

References 32 publications

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“…This result suggested that the radiomics signature reinforced the prognostic ability of the staging system, thereby adding prognostic value to clinicopathologic risk factors. The concept of this combination model has been described previously (34,44). However, the combination model mentioned earlier was employed to facilitate personalized treatment for patients with HB and provide pediatricians with a powerful tool for making clinical decisions.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Jiang

Sun

Xia³

et al. 2021

Front. Oncol.

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Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model.Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a “radiomics signature” and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration.Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738–0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829–0.933).Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Jiang

Sun

Xia³

et al. 2021

Front. Oncol.

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“…Feature Selection 2 (FS2) excludes one of the correlated features from the analysis as redundant based on the correlation coefficients calculated by Pearson's correlation analysis for all features [21,31]. An absolute value of the correlation coefficient of 0.8 or greater was the threshold to indicate strong correlation between two features [31,32].…”

Section: Feature Selectionmentioning

confidence: 99%

“…In the abovementioned study, there was minimum evaluation of the improvement in prediction performance by subgroup analysis because the analysis group was too limited. Yang et al [21] developed and validated a radiomic method by integrating tumor and lymph node radiomics for the preoperative prediction of lymph node status in gastric cancer. They performed validation using subgroups in the test dataset and showed an improvement in prediction performance compared to the validation using the whole dataset.…”

Section: Introductionmentioning

confidence: 99%

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

et al. 2021

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Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. Methods A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. Results In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Conclusions Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.

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“…Studies have shown that radiomics, the technique of converting medical images into mineable data and high-dimensional features, has been proven to improve diagnostic and prognostic accuracy in oncology [23][24][25][26]. It had been widely applied to the prediction of LN metastasis in GC, colorectal cancer and occult peritoneal metastasis in advanced GC and achieved satisfactory results [27][28][29][30][31][32][33][34][35].…”

mentioning

confidence: 99%

“…At present, the published radiomics research in finding the predictors of LNM mainly use tumor radiomics characteristics or other characteristics related to patients. However, the ability to accurately predict LNM may be affected by relying solely on the radiomics characteristics of primary tumors [33]. Thus, this study aimed to predict preoperative LNM in T1-2 GC patients by integrating the radiomics characteristics of LN and primary tumors.…”

mentioning

confidence: 99%

Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer

Wang

Fang

et al. 2021

BMC Med Imaging

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Background This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients. Methods A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve. Results Two radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832–0.998) and testing cohort (AUC 0.908; 95% CI 0.814–1.000). The decision curve also indicated its potential clinical usefulness. Conclusions The nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

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Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

Cited by 42 publications

References 32 publications

Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer

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