Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer

MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy

OBJECTIVE

This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.

METHODS

We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).

RESULTS

Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.

CONCLUSIONS

The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.

Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma

RATIONALE AND OBJECTIVES

To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats.

MATERIALS AND METHODS

The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC).

RESULTS

Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor.

CONCLUSION

Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.

Preoperative prediction of lymph node metastasis in endometrial cancer patients via an intratumoral and peritumoral multiparameter MRI radiomics nomogram

Introduction

While lymph node metastasis (LNM) plays a critical role in determining treatment options for endometrial cancer (EC) patients, the existing preoperative methods for evaluating the lymph node state are not always satisfactory. This study aimed to develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter magnetic resonance imaging (MRI) features to preoperatively predict LNM in EC patients.

Methods

Three hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and independent validation (n = 60) cohorts. Radiomic features were extracted from intra- and peritumoral regions via axial T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) mapping. A radiomics model (annotated as the Radscore) was established using the selected features from different regions. The clinical parameters were statistically analyzed. A nomogram was developed by combining the Radscore and the most predictive clinical parameters. Decision curve analysis (DCA) and the net reclassification index (NRI) were used to assess the clinical benefit of using the nomogram.

Results

Nine radiomics features were ultimately selected from the intra- and peritumoral regions via ADC mapping and T2WI. The nomogram combining the Radscore, serum CA125 level, and tumor area ratio achieved the highest AUCs in the training, test and independent validation sets (nomogram vs. Radscore vs. clinical model: 0.878 vs. 0.850 vs. 0.674 (training), 0.877 vs. 0.838 vs. 0.668 (test), and 0.864 vs. 0.836 vs. 0.618 (independent validation)). The DCA and NRI results revealed the nomogram had greater diagnostic performance and net clinical benefits than the Radscore alone.

Conclusion

The combined intra- and peritumoral region multiparameter MRI radiomics nomogram showed good diagnostic performance and could be used to preoperatively predict LNM in patients with EC.

The value of multiparameter MRI of early cervical cancer combined with SCC-Ag in predicting its pelvic lymph node metastasis

Purpose

To investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM).

Material and methods

115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC.

Results

The PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s2 vs.1.06 ± 0.35 mm/s2, P<0.001), ADCmin [0.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively.

Conclusions

Quantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved.

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma

OBJECTIVES

This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.

RESULTS

The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).

CONCLUSION

The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.

CRITICAL RELEVANCE STATEMENT

The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.

KEY POINTS

The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.

Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis

Background

The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC).

Methods

A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC.

Results

A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively.

Conclusion

MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.

Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy

PURPOSES

To investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC).

MATERIALS AND METHODS

In this multicentre study, 700 patients with IB2-IVA cervical cancer who underwent CCRT with ongoing follow-up were retrospectively analyzed. Three-dimensional radiomics features of primary lesions and its surrounding 5 mm region in T2WI sequences were collected. Six machine learning methods were used to construct the optimal radiomics model for accurate prediction of DFS and OS after CCRT in LACC patients. Eventually, TCGA and GEO databases were used to explore the mechanisms of radiomics in predicting the progression and survival of cervical cancer. This study adhered CLEAR for reporting and its quality was assessed using RQS and METRICS.

RESULTS

In the prediction of DFS, the RSF model combined tumor and peritumor radiomics demonstrated the best predictive efficacy, with the AUC for predicting 1-year, 3-year, and 5-year DFS in the training, validation, and test sets of 0.986, 0.989, 0.990, and 0.884, 0.838, 0.823, and 0.829, 0.809, 0.841, respectively. In the prediction of OS, the GBM model best performer, with AUC of 0.999, 0.995, 0.978, and 0.981, 0.975, 0.837, and 0.904, 0.860, 0.905. Differential genes in TCGA and GEO suggest that the prediction of radiomics model may be associated with KDELR2 and HK2.

CONCLUSION

Machine learning-based radiomics models help to predict DFS and OS after CCRT in LACC patients, and the combination of tumor and peritumor information has higher predictive efficacy, which can provide a reliable basis for therapeutic decision-making in cervical cancer patients.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Background

Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs.

Methods

Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC).

Findings

The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91).

Interpretation

Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).