Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma

Tan, Xian-Zheng; Ma, Zhanjun; Lu, Yan; Ye, Weitao; Liu, Zaiyi; Liang, Changhong

doi:10.1007/s00330-018-5581-1

Cited by 92 publications

(75 citation statements)

References 34 publications

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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%

“…Model 1 was developed with CT-reported LN status and handcraftedradiomics signature, showing the discrimination with C-statistic of 0.728 in external validation cohort. In recent studies, Tan et al (27) and Shen et al (35) also developed a similar radiomics nomogram, which presented an AUC of 0.773 and 0.771 in the validation cohort, respectively. Although the effect of their handcrafted radiomics model was superior to Model 1 of our research, they did not have external validation.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

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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“…NRI and IDI are more-sensitive indicators than the C-index [25,26]. NRI re ects how nomogram can better reclassify risk probability than AJCC staging system, while IDI re ects the improvement of distinguishing ability between nomogram and AJCC staging system.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for predicting cancer-specific survival of patients with buccal squamous cell carcinoma: a population-based study

Wang

chu²,

Yang

et al. 2020

Preprint

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Background The use of the traditional American Joint Committee on Cancer (AJCC) staging system alone has limitations in predicting the survival of buccal squamous cell carcinoma (BSCC) patients. We aimed to establish a comprehensive prognostic nomogram whose prognostic value compared with AJCC system. Methods Patients were obtained from the SEER (Surveillance, Epidemiology, and End Result) database. The Cox regression model was used to select variables using a backward stepwise selection method. Based on the predictive model for determining prognostic factors, a nomogram was developed to predict the cancer-specific survival rates of BSCC patients at 3, 5 and 8 years. We used several standard model validation methods to evaluate the performance of the survival model: concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification improvement (NRI), calibration plots, decision curve analyses (DCAs), and integrated discrimination improvement (IDI). Results Multivariate analysis showed that age, AJCC stage, N stage, M stage, pathological grade, and surgical status were risk factors for survival. The c-index chart, the AUC chart, and the calibration chart show that the performance of the nomogram is better, and the NRI and IDI values show that the performance of the nomogram is better than that of the AJCC staging system. The 3-year, 5-year and 8-year DCA curves of nomogram show that it has more net benefit than traditional AJCC staging system, both in terms of training and validation queues. Conclusion We developed and validated the first BSCC prognosis nomogram, which has a better prognostic value than the separate AJCC staging system. Our BSCC prognosis nomogram is a valuable tool for explaining the risks of the next 3, 5 and 8 years in clinical practice.

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“…Nomograms have been used in most cancer types in recent years [25][26][27][28][29][30][31][32] , including for ccRCC [33][34][35] . However, there has been a lack of overall evaluations of the developed nomograms, or the variables used for prediction have not been readily available.…”

Section: Discussionmentioning

confidence: 99%

A practical nomogram for predicting cancer-specific survival in patients with clear-cell renal cell carcinoma

Wen

Wang

Bai

et al. 2020

Preprint

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Background: It has limitations in predicting patient cancer-specific survival to use of the traditional American Joint Committee on Cancer (AJCC) staging system alone. Objectives: We aimed to establish and evaluate a comprehensive prognostic nomogram and compare its prognostic value with the AJCC-7th staging system in adults diagnosed with ccRCC.Methods: We used the SEER database to identify 24477 cases of ccRCC between 2010 and 2015. In the development cohort, we used multivariate Cox proportional-hazards analyses to select significant variables, and used R software to establish a nomogram for predicting the 3-year and 5-year cancer-specific survival rates of ccRCC patients. In the development and validation cohorts, we compared our cancer-specific survival model with the AJCC-7th prognosis model to evaluate the performance of the nomogram by calculating the concordance index (C-index), Youden Index, area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and performing calibration plotting and decision curve analyses (DCAs). Results: Eleven identified independent prognostic factors were used to establish the nomogram. Age at diagnosis, being unmarried, higher grades, larger tumor size, higher AJCC-7th stage, lymph node metastases, bone metastases, liver metastases, lung metastases, radiotherapy, and no surgery were risk factors for the cancer-specific survival of ccRCC. The C-index, Youden Index, AUC, NRI, IDI, and calibration plots demonstrated the good performance of the nomogram compared to the AJCC-7th staging system. Moreover, the 3-year and 5-year DCA curves showed that the nomogram yielded net benefits that were greater than the traditional AJCC-7th staging system. Conclusion: This study is the first to indicate that married status is an important prognostic parameter in ccRCC. Our results also demonstrate that the developed nomogram can predict cancer-specific survival more accurately than the AJCC-7th staging system alone. The prognostic factors were easily obtained.

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Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma

Abstract: • A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC. • The radiomics nomogram outperformed size criteria. • Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.

Cited by 92 publications

References 34 publications

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

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

Nomogram for predicting cancer-specific survival of patients with buccal squamous cell carcinoma: a population-based study

A practical nomogram for predicting cancer-specific survival in patients with clear-cell renal cell carcinoma

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