MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma

Long, Ling; Sun, Jianqing; Jiang, Liling; Hu, Yixin; Li, Lan; Tan, Yong; Cao, Meimei; Xiao, Lan; Zhang, Jiuquan

doi:10.1016/j.diii.2021.02.008

Cited by 43 publications

(27 citation statements)

References 29 publications

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“…Medical Oncology considers FIGO IA the only low-risk stage (24). The methodologic and design contributions relative to the other recent radiomics studies referred to earlier (15)(16)(17)(18)(19)(20)(21)(22) can be summarized as follows: first, latest recommendations from the Image Biomarker Standardization Initiative (or IBSI) on the development of robust and reproducible radiomics models were applied (25); second, a full 3D analysis was implemented at multiparametric MRI; and finally, proposed machine learning models were validated on an independent external test set and compared with preoperative readings from radiologists.…”

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confidence: 99%

Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Lefebvre¹,

Ueno²,

Dohan³

et al. 2022

Radiology

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confidence: 99%

Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Lefebvre¹,

Ueno²,

Dohan³

et al. 2022

Radiology

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“…Firstly, the radiologist’s eyes and biopsy specimens cannot identify LVSI, which assumes a key role in the risk stratification of EC. In fact, LVSI correlates with a higher frequency of tumor recurrence and lymph node metastasis, a reduced PFS and OS; therefore, its evaluation may lead toward adjuvant therapy even in EC that were preoperatively misclassified as early-stage tumors [ 20 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer

Celli

Guerreri

Pernazza³

et al. 2022

Cancers

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High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by “ProMisE”. This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management.

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“…Yan Luo et al developed a multiparametric MRI-based radiomics nomogram to assess LVSI preoperatively for providing decision-making support [ 33 ]. In Ling Long et al’s study, the computer-vision signature was added to construct a nomogram, which showed better predictive ability compared with the model based on traditional handcrafted radiomics features [ 34 ]. It was reported that single pathological characteristics, including tumor diameter, depth of myometrial invasion, tumor grade, and cervical stromal involvement, were incorporated in an innovative score called the LVSI index to determine LVSI [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

Wang

Yang

et al. 2022

IJERPH

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Objective: This study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients. Methods: We included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People’s Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness. Results: Independent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050–3.070, p = 0.032) (G3: OR = 3.49, 95% CI: 1.870–6.520, p < 0.001), histology (OR = 2.723, 95% CI: 1.370–5.415, p = 0.004), MI (OR = 4.286, 95% CI: 2.663–6.896, p < 0.001), and MRS (OR = 1.124, 95% CI: 1.067–1.185, p < 0.001) in the training cohort. A nomogram was established to predict a patient’s probability of developing LVSI based on these factors. The ROC curve analysis showed that an MRS-based nomogram significantly improved the efficiency of diagnosing LVSI compared with the nomogram based on clinicopathological factors (p = 0.0376 and p = 0.0386 in the training and validation cohort, respectively). Subsequently, the calibration plot showed a favorable consistency in both groups. Moreover, we conducted a decision curve analysis, showing the great clinical benefit obtained from the application of our nomogram. However, our study faced several limitations. Further external validation and a larger sample size are needed in future studies. Conclusion: MRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.

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MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma

Cited by 43 publications

References 29 publications

Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

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