MRI‐based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma

Zhao, Mingli; Wen, Feng; Shi, Jiaxin; Song, Jing; Zhao, Jiaqi; Song, Qingling; Lai, Qingyuan; Luo, Yahong; Yu, Tao; Jiang, Xiran; Jiang, Wenyan; Dong, Yue

doi:10.1002/mp.15835

Cited by 18 publications

(7 citation statements)

References 74 publications

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“…The FIGO stage is a more widely used and authoritative staging system; according to stage III of the disease, the tumor has invaded the uterine serosal layer or fallopian tubes. For these patients, the rate of postoperative recurrence and distant metastasis is high, even with aggressive surgical treatment, ultimately worsening the patient’s prognosis ( 13 , 14 ). When the tumor thrombus enters the blood or lymphatic vessels, vascular tumor thrombus may lead to lymph node metastasis and, also, distant metastasis ( 15 , 16 ).…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, we also established a nomogram prediction model to more intuitively identify the high risk group of tumor-specific death in patients with EC 5 years after surgery and found that the predictive model was reliable. Nomogram predictive models can accurately identify the high risk group of poor prognosis in a variety of diseases ( 13 , 19 - 22 ). Some authors have discussed on the role of nomogram predictive models in evaluating patients with EC and found that predictive models have good evaluation value ( 3 , 4 ).…”

Section: Discussionmentioning

confidence: 99%

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A postoperative tumor-specific death prediction model for patients with endometrial cancer: a retrospective study

Chen,

Yan,

et al. 2024

Transl Cancer Res

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Background Endometrial cancer (EC) is an epithelial malignancy occurring in the endometrium, with a 5-year mortality rate of above 10%. However, there is currently a lack of studies exploring the potential of a predictive model of tumor-specific death after surgery in these patients. Methods From January 2015 to December 2017, data related to 482 patients with EC admitted to the Dushu Lake Hospital Affiliated to Soochow University were analyzed. Patients were divided into death (n=62) and survival (n=420) groups according to whether tumor-specific death occurred at 5 years postoperatively or not. The clinical characteristics of the two groups were compared, and the risk factors for tumor-specific death in patients with EC 5 years after surgery were investigated by logistics regression analysis. A nomogram prediction model was established according to the relevant risk factors. Results Tumor size, Ki-67 positive rate, Federation International of Gynecology and Obstetrics (FIGO) stage, and the rate of vascular tumor thrombus between the two groups (P<0.05) were found to be the statistically significant factors. Positive Ki-67, tumor size >3.35 cm, stage III, and vascular tumor thrombus were factors that influenced the tumor-specific death at 5 years after surgery (P<0.05). The predictive model obtained an area under the receiver operating characteristic (ROC) curves in the training and verification sets of 0.847 [95% confidence interval (CI): 0.779–0.916] and 0.886 (95% CI: 0.803–0.969), respectively. Conclusions The nomogram prediction model, which was established in this study, was proved to be valuable in predicting tumor-specific death 5 years after the surgery in patients with EC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A postoperative tumor-specific death prediction model for patients with endometrial cancer: a retrospective study

Chen,

Yan,

et al. 2024

Transl Cancer Res

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“…In the present findings, MRS, histological type, LNM and tumor grade were independent factors, and these factors were used to build the model. To the best of our knowledge, there only a few predictive models have been constructed to evaluate MI before surgery, and they are mostly based on MRI radiomics (7,17). The aforementioned studies used parameters such as axial T1-weighted images (T1WIs), T2WIs and diffusion weighted imaging to sketch region of interest, and further least absolute shrinkage and selection operator regression was conducted to narrow the range.…”

Section: Discussionmentioning

confidence: 99%

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

2023

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The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

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“…Cerebral alveolar echinococcosis is a rare parasitic disease, but it is still a severe public health issue in many parts of the world. We believe that radiomics-based machine learning is a novel tool to investigate this disease, which have been proved as a powerful approach in other fields [ 38 – 41 ].…”

Section: Discussionmentioning

confidence: 99%

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

Yimit,

Yasin,

Tuersun

et al. 2023

Eur J Med Res

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Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. Purpose This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. Methods We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). Results The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. Conclusion The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

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MRI‐based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma

Cited by 18 publications

References 74 publications

A postoperative tumor-specific death prediction model for patients with endometrial cancer: a retrospective study

A postoperative tumor-specific death prediction model for patients with endometrial cancer: a retrospective study

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

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