Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

BACKGROUND AND PURPOSE

Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC.

MATERIAL AND METHODS

212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments.

RESULTS

A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC.

CONCLUSION

In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics

OBJECTIVES

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer.

METHODS

A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves.

RESULTS

Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively.

CONCLUSIONS

The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients.

ADVANCES IN KNOWLEDGE

This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study

PURPOSE

To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification.

METHODS

Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility.

RESULTS

The DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896-0.992) and 0.885 (95% confidence interval 0.834-0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit.

CONCLUSION

A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.

Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy

This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models-the clinical model, the radiomics model, and the combined clinic and radiomics model-were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

BACKGROUND

Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients.

METHODS

Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics.

RESULTS

The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively.

CONCLUSION

Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BACKGROUND

Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.

METHODS

A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.

RESULTS

Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.

CONCLUSION

The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

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

BACKGROUND

Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM.

METHODS

A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM.

RESULTS

The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC.

CONCLUSIONS

 Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.

Development of a nomogram for predicting pelvic lymph node metastasis in cervical squamous cell carcinoma

OBJECTIVE

To develop and validate a nomogram for predicting pelvic lymph node metastasis (LNM) in cervical squamous cell carcinoma (SCC).

METHODS

This was a retrospective study that included 715 patients with cervical SCC who underwent radical hysterectomy and bilateral pelvic lymphadenectomy between 2009 and 2018. Logistic regression analysis was used to identify independent risk factors for pelvic LNM. Based on these risk factors, a nomogram predicting LNM risk was constructed and internally validated using the bootstrapping resampling method.

RESULTS

The rate of LNM in FIGO (the International Federation of Gynecology & Obstetrics) Stage IA2-IIA2 cervical SCC was 24.2%. In multivariate analysis, FIGO Stage II, moderately differentiated or poorly differentiated histology, abnormally elevated serum SCC-antigen, and triglyceride were identified as independent risk factors for LNM. Tumor size greater than 2 cm and parametrial involvement had borderline significance. Ultimately, the nomogram contained the six variables mentioned above, showing positive calibration and positive discrimination. The area under the receiver operating characteristic curvewas 0.827 and the bootstrap-validated C-index was 0.827. The Youden index of this paper was 0.540.

CONCLUSIONS

We developed and validated a nomogram to predict pelvic LNM in SCC based on clinical data, which can help physicians develop an optimal treatment strategy.

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

Objectives

To investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.

Methods

This retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.

Results

173 patients (mean age 43.1 years, range 18-69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.

Conclusions

The ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

Reduced field-of-view DWI‑derived clinical-radiomics model for the prediction of stage in cervical cancer

BACKGROUND

Pretreatment prediction of stage in patients with cervical cancer (CC) is vital for tailoring treatment strategy. This study aimed to explore the feasibility of a model combining reduced field-of-view (rFOV) diffusion-weighted imaging (DWI)-derived radiomics with clinical features in staging CC.

METHODS

Patients with pathologically proven CC were enrolled in this retrospective study. The rFOV DWI with b values of 0 and 800 s/mm² was acquired and the clinical characteristics of each patient were collected. Radiomics features were extracted from the apparent diffusion coefficient maps and key features were selected subsequently. A clinical-radiomics model combining radiomics with clinical features was constructed. The receiver operating characteristic curve was introduced to evaluate the predictive efficacy of the model, followed by comparisons with the MR-based subjective stage assessment (radiological model).

RESULTS

Ninety-four patients were analyzed and divided into training (n = 61) and testing (n = 33) cohorts. In the training cohort, the area under the curve (AUC) of clinical-radiomics model (AUC = 0.877) for staging CC was similar to that of radiomics model (AUC = 0.867), but significantly higher than that of clinical model (AUC = 0.673). In the testing cohort, the clinical-radiomics model yielded the highest predictive performance (AUC = 0.887) of staging CC, even without a statistically significant difference when compared with the clinical model (AUC = 0.793), radiomics model (AUC = 0.846), or radiological model (AUC = 0.823).

CONCLUSIONS

The rFOV DWI-derived clinical-radiomics model has the potential for staging CC, thereby facilitating clinical decision-making.

RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study

BACKGROUND

It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer.

METHODS

A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models' performance was analyzed by the receiver operating characteristic analysis in the test cohort.

RESULTS

Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774-0.906] and test cohort (AUC 0.767; 95% CI 0.613-0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821-0.938) and test cohort (AUC 0.844; 95% CI 0.701-0.936).

CONCLUSION

The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

Background

The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation.

Purpose

To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC.

Material and Methods

In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values.

Results

Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively).

Conclusion

Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

OBJECTIVE

The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters.

METHODS

In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels.

RESULTS

The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation.

CONCLUSION

The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis

OBJECTIVE

To evaluate and compare the diagnostic performance of apparent diffusion coefficient (ADC) values and MRI-based radiomics analysis for lymph node metastasis (LNM) detection in patients with cervical cancer (CC).

METHODS

We searched relevant databases for studies on ADC values and MRI-based radiomics analysis for LNM detection in CC between January 2001 and December 2021. Methodological quality assessment of risk of bias using Quality Assessment of Diagnostic Accuracy Studies 2 and radiomics quality score (RQS) of the studies was conducted. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Diagnostic performance was compared between the two quantitative analyses using a two-sample Z-test.

RESULTS

In total, 22 studies including 2314 patients were included. Unclear risk of bias was observed in 4.5-36.4% of the studies. The 8 radiomics studies exhibited a median (interquartile range) RQS of 13.5 (5.5-15.75). The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC of the ADC values vs radiomics analysis were 0.86 vs 0.84, 0.85 vs 0.73, 5.7 vs 3.1, 0.17 vs 0.22, 34 vs 14, and 0.91 vs 0.86, respectively. There was no threshold effect or publication bias, but significant heterogeneity existed among the studies. No significant difference was detected in the diagnostic performance of the two quantitative analyses using the Z-test.

CONCLUSION

ADC values are more clinically promising because they are more easily accessible and widely applied, and exhibit a non-statistically significant trend to outperform radiomics analysis.

MRI Texture Analysis for Preoperative Prediction of Lymph Node Metastasis in Patients with Nonsquamous Cell Cervical Carcinoma

RATIONALE AND OBJECTIVES

To preoperatively predict lymph node metastasis (LNM) in patients with cervical nonsquamous cell carcinoma (non-SCC) based on magnetic resonance imaging (MRI) texture analysis.

MATERIALS AND METHODS

This retrospective study included 104 consecutive patients (mean age of 47.2 ± 11.3 years) with stage IB-IIA cervical non-SCC. According to the ratio of 7:3, 72, and 32 patients were randomly divided into the training and testing cohorts. A total of 272 original features were extracted. In the process of feature selection, features with intraclass correlation coefficients (ICCs) less than 0.8 were eliminated. The Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were applied to reduce redundancy, overfitting, and selection biases. Further, a support vector machine (SVM) with linear kernel function was applied to select the optimal feature set with a high discrimination power.

RESULTS

The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI (LN status on MRI)-based SVM models yielded an AUC and accuracy of 0.78 and 0.79; 0.79 and 0.69; 0.79 and 0.81 for predicting LNM in the training cohort, and 0.82 and 0.78; 0.82 and 0.69; 0.79 and 0.72 in the testing cohort. The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models performed better than morphologic criteria of LNS-MRI and yield similar discrimination abilities in predicting LNM in the training and testing cohorts (all p-value > 0.05). In addition, the T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the AC and ASC subgroups (all p-value > 0.05).

CONCLUSION

The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI+DWI+LNS-MRI-based SVM models showed similar good discrimination ability and performed better than the morphologic criteria of LNS-MRI in predicting LNM in patients with cervical non-SCC. The inclusion of the CE-T1WI sequence and morphologic criteria of LNS-MRI did not significantly improve the performance of the T2WI + DWI-based model. The T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the subgroup analysis.

Post treatment imaging in patients with local advanced cervical carcinoma

Cervical cancer (CC) is the fourth leading cause of death in women worldwide and despite the introduction of screening programs about 30% of patients presents advanced disease at diagnosis and 30-50% of them relapse in the first 5-years after treatment. According to FIGO staging system 2018, stage IB3-IVA are classified as locally advanced cervical cancer (LACC); its correct therapeutic choice remains still controversial and includes neoadjuvant chemo-radiotherapy, external beam radiotherapy, brachytherapy, hysterectomy or a combination of these modalities. In this review we focus on the most appropriated therapeutic options for LACC and imaging protocols used for its correct follow-up. We explore the imaging findings after radiotherapy and surgery and discuss the role of imaging in evaluating the response rate to treatment, selecting patients for salvage surgery and evaluating recurrence of disease. We also introduce and evaluate the advances of the emerging imaging techniques mainly represented by spectroscopy, PET-MRI, and radiomics which have improved diagnostic accuracy and are approaching to future direction.

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Purpose

This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.

Materials and Methods

We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).

Results

Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.

Conclusions

The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

Nomogram Predicting Lymph Node Metastasis in the Early-Stage Cervical Cancer

Background

Accurately predicting the risk level of lymph node metastasis is essential for the treatment of patients with early cervical cancer. The purpose of this study is to construct a new nomogram based on 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and clinical characteristics to assess early-stage cervical cancer patients’ risk of lymph node metastasis.

Materials and Methods

From January 2019 to November 2020, the records of 234 patients with stage IA-IIA [International Federation of Gynecology and Obstetrics (FIGO) 2018] cervical cancer who had undergone PET/CT examination within 30 days before surgery were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed based on it. The nomogram was developed and validated by internal and external validation. The validation cohorts included 191 cervical cancer patients from December 2020 to October 2021.

Results

Four factors [squamous cell carcinoma associated antigen (SCCA), maximum standardized uptake value of lymph node (nSUVmax), uterine corpus invasion in PET/CT and tumor size in PET/CT] were finally determined as the predictors of the nomogram. At the area under the receiver operating characteristic curve cohort was 0.926 in the primary and was 0.897 in the validation cohort. The calibration curve shows good agreement between the predicted probability and the actual probability. The decision curve analysis showed the clinical utility of the nomogram.

Conclusion

We had established and verified a simple and effective nomogram, which can be used to predict the lymph node metastasis of cervical cancer patients before surgery.

Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer

OBJECTIVE

To develop a radiomics nomogram based on multiparametric MRI (mpMRI) to pre-operatively predict lymph-vascular space invasion (LVSI) in patients with early-stage cervical cancer.

METHODS

This retrospective study included 233 consecutive patients with Stage IB-IIB cervical cancer. According to the ratio of 2:1, 154 patients and 79 patients were randomly assigned to the primary and validation cohorts, respectively. Features with intraclass and interclass correlation coefficient (ICCs) greater than 0.75 were selected for radiomics features. The significant features for predicting LVSI were selected using the least absolute shrinkage and selection operator (LASSO) algorithm based on the primary cohort. The rad-score for each patient was constructed via a linear combination of selected features that were weighted by their respective coefficients. The radiomics nomogram was developed using multivariable logistic regression analysis by incorporating the rad-score and clinical risk factors.

RESULTS

A total of 19 radiomics features and 3 clinical risk factors were selected. The rad-score exhibited a good performance in discriminating LVSI with a C-index of 0.76 and 0.81 in the primary and validation cohorts, respectively. The radiomics nomogram also exhibited a good discriminating performance in two cohorts (C-index of 0.78 and 0.82). The calibration curve of the radiomics nomogram demonstrated no significant differences was found between prediction and observation outcomes for the probability of LVSI in two cohorts (p = 0.86 and 0.98, respectively). The decision curve analysis indicated that clinician and patients could benefit from the use of radiomics nomogram and rad-score.

CONCLUSION

The nomogram and rad-score could be used conveniently and individually to predict LVSI in patients with early-stage cervical cancer and facilitate the treatment decision for clinician and patients.

ADVANCES IN KNOWLEDGE

The nomogram could pre-operatively predict LVSI in early-stage cervical cancer.

Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

Simple Summary

Uterine cervical cancer (CC) is a leading cause of cancer-related deaths in women worldwide. Pelvic magnetic resonance imaging (MRI) allows the assessment of local tumor extent and guides the choice of primary treatment. MRI tumor segmentation enables whole-volume radiomic tumor profiling, which is potentially useful for prognostication and individualization of therapy in CC. Manual tumor segmentation is, however, labor intensive and thus not part of routine clinical workflow. In the current work, we trained a deep learning (DL) algorithm to automatically segment the primary tumor in CC patients. Although the achieved segmentation performance of the trained DL algorithm is slightly lower than that for human experts, it is still relatively good. This study suggests that automated MRI primary tumor segmentations by DL algorithms without any human interaction is possible in patients with CC.

Abstract

Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.

Added value of radiomics analysis in MRI invisible early-stage cervical cancers

OBJECTIVES

To determine the diagnostic ability of cervical mucosa radiomics signature of sagittal T2WI and T1 contrast-enhanced (CE) imaging in detecting early-stage cervical cancers with negative MRI.

METHODS

Preoperative images of postoperative pathology confirmed early-stage cervical cancer patients and normal cervix patients admitted to our hospital between January 2013 and December 2020 were retrospectively reviewed. Patients with cancer signals on T2WI, T1CE and DWI were deleted. Regions of interests (ROIs) were delineated on cervical mucosa (from cervical canal to cervical dome) with 5 mm width on sagittal T2WI and T1CE. The maximum-relevance and minimumredundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the calculation of radiomics signature scores. Diagnostic performance was assessed and compared between radiomics prediction models (model 1: T1CE; model 2: T2WI; model 3: model one combined with model 2). Differential diagnostic ability of radiomics signature in detecting lymphatic vascular space invasion (LVSI) was further explored.

RESULTS

Diagnostic performance of model three was higher than model 1 and model 2 both in primary (model 3 0.874, model 1 0.857, model 2 0.816) and validation (model 3 0.853, model 1 0.847, model 2 0.634) cohorts. Model 3 showed statistical diagnostic difference compared with model 2 (primary p = 0.008, validation p = 0.000). However, the diagnostic improvement ability of model 3 showed no statistical difference compared with model 1 (primary p = 0.351, validation p = 0.739). Diagnostic efficiency of model 3 in detecting LVSI was not apparent (AUC 0.64).

CONCLUSIONS

Radiomics analysis of cervical mucosa combining T1CE and T2WI is promising for predicting MRI invisible early-stage cervical cancers, however further ability in detecting LVSI was not apparent.

ADVANCES IN KNOWLEDGE

Conventional MRI was originally defined as meaningless in very early-stage cervical cancers. However, whether MRI radiomics analysis of cervical mucosa can detecting tiny changes of invisible early stage cervical cancers has not been researched yet.

Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer

Purpose

The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients.

Procedures

A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort.

Results

Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility.

Conclusions

The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00450-w.

Challenges in ensuring the generalizability of image quantitation methods for MRI

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer

Background

The depth of cervical stromal invasion is one of the important prognostic factors affecting decision-making for early stage cervical cancer (CC). This study aimed to develop and validate a T2-weighted imaging (T2WI)-based radiomics model and explore independent risk factors (factors with statistical significance in both univariate and multivariate analyses) of middle or deep stromal invasion in early stage CC.

Methods

Between March 2017 and March 2021, a total of 234 International Federation of Gynecology and Obstetrics IB1-IIA1 CC patients were enrolled and randomly divided into a training cohort (n = 188) and a validation cohort (n = 46). The radiomics features of each patient were extracted from preoperative sagittal T2WI, and key features were selected. After independent risk factors were identified, a combined model and nomogram incorporating radiomics signature and independent risk factors were developed. Diagnostic accuracy of radiologists was also evaluated.

Results

The maximal tumor diameter (MTD) on magnetic resonance imaging was identified as an independent risk factor. In the validation cohort, the radiomics model, MTD, and combined model showed areas under the curve of 0.879, 0.844, and 0.886. The radiomics model and combined model showed the same sensitivity and specificity of 87.9% and 84.6%, which were better than radiologists (sensitivity, senior = 75.7%, junior = 63.6%; specificity, senior = 69.2%, junior = 53.8%) and MTD (sensitivity = 69.7%, specificity = 76.9%).

Conclusion

MRI-based radiomics analysis outperformed radiologists for the preoperative diagnosis of middle or deep stromal invasion in early stage CC, and the probability can be individually evaluated by a nomogram.

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer

OBJECTIVES

To develop and validate a clinical-radiomics model that incorporates radiomics signatures and pretreatment clinicopathological parameters to identify multimodality therapy candidates among patients with early-stage cervical cancer.

METHODS

Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training (n = 194, training:validation = 8:2) and testing (n = 41) sets according to surgical time. The radiomics features of each patient were extracted from preoperative sagittal T2-weighted images. Significance testing, Pearson correlation analysis, and Least Absolute Shrinkage and Selection Operator were used to select radiomic features associated with multimodality therapy administration. A clinical-radiomics model incorporating radiomics signature, age, 2018 Federation International of Gynecology and Obstetrics (FIGO) stage, menopausal status, and preoperative biopsy histological type was developed to identify multimodality therapy candidates. A clinical model and a clinical-conventional radiological model were also constructed. A nomogram and decision curve analysis were developed to facilitate clinical application.

RESULTS

The clinical-radiomics model showed good predictive performance, with an area under the curve, sensitivity, and specificity in the testing set of 0.885 (95% confidence interval: 0.781-0.989), 78.9%, and 81.8%, respectively. The AUC, sensitivity, and specificity of the clinical model and clinical-conventional radiological model were 0.751 (0.603-0.900), 63.2%, and 63.6%, 0.801 (0.661-0.942), 73.7%, and 68.2%, respectively. A decision curve analysis demonstrated that when the threshold probability was > 20%, the clinical-radiomics model or nomogram may be more advantageous than the treat all or treat-none strategy.

CONCLUSIONS

The clinical-radiomics model and nomogram can potentially identify multimodality therapy candidates in patients with early-stage cervical cancer.

KEY POINTS

• Pretreatment identification of multimodality therapy candidates among patients with early-stage cervical cancer helped to select the optimal primary treatment and reduce severe complication risk and costs. • The clinical-radiomics model achieved a better prediction performance compared with the clinical model and the clinical-conventional radiological model. • An easy-to-use nomogram exhibited good performance for individual preoperative prediction.

An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer

OBJECTIVES

To investigate the prognostic role of magnetic resonance imaging (MRI)-based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer.

METHODS

A total of 207 cervical cancer patients (training cohort: n = 144; validation cohort: n = 63) were enrolled. 792 radiomics features were extracted from T2W and diffusion-weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps.

RESULTS

A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS (p < 0.05) and DFS (p < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3-year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics.

CONCLUSIONS

The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application.

ADVANCES IN KNOWLEDGE

This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early-stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.

Combination of Estrogen Receptor Alpha and Histological Type Helps to Predict Lymph Node Metastasis in Patients with Stage IA2 to IIA2 Cervical Cancer

Objective

This study aimed to identify a subset of patients with stage IA2 to IIA2 cervical cancer who are at low risk of lymph node metastasis (LNM) using pathological parameters including estrogen receptor alpha (ERα) and progesterone receptor (PR).

Methods

The clinical data of patients with stage IA2 to IIA2 cervical cancer who underwent radical surgery between 2014 and 2015 were retrospectively reviewed. Immunohistochemical staining was used to determine the expression of ERα and PR. A low-risk criterion for LNM was identified using logistic regression analysis, and its performance was estimated through receiver-operating characteristic curve analysis.

Results

Of 263 patients, 57 (21.7%) had pathological LNM. ERα (adjusted odds ratio [aOR], 7.582; 95% confidence interval [CI], 2.991–19.222; P < 0.001) and squamous cell carcinoma (aOR, 3.520; 95% CI, 1.887–6.568; P < 0.001) were identified as independent predictors for no LNM by multivariate logistic regression analysis, while PR had no effect on LNM. The rate of LNM was 1.4% for low-risk patients (n = 73) identified as ERα positive with squamous cell carcinoma. The 5-year disease-free survival in low-risk patients was significantly greater than in those negative for ERα and/or those with non-squamous cell carcinoma (96.9% vs 80.1%, P = 0.002).

Conclusion

ERα positivity and squamous cell carcinoma are associated with a low risk of LNM in patients with stage IA2 to IIA2 cervical cancer. Hence, those patients without a low risk of LNM could be considered for definitive chemoradiotherapy to avoid unnecessary surgery.

MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

PURPOSE

To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses.

METHODS

We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters.

RESULTS

Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models.

CONCLUSION

The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.

Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

RATIONALE AND OBJECTIVES

Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients.

METHODS

In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets.

RESULTS

The radiomic signature was significantly associated with the lateral LNM in both cohorts (p< 0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application.

CONCLUSION

The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients.

Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer

OBJECTIVE

To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC).

METHODS

A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOI^entire, VOI^+5 mm, and VOI^-5 mm) to build 3 rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to build combined model incorporating rad-scores with clinical risk factors and compared with clinical model alone. Kaplan-Meier analysis was used to further validate prognostic value of selected clinical and radiomics characteristics.

RESULTS

Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI^+5 mm) performed best with a C-index of 0.750 in the training set and 0.839 in the validation set. Combined model was constructed by incorporating age categorized by 55, Federation of Gynecology and Obstetrics (Figo) stage, and lymphovascular space invasion with rad-score (VOI^+5 mm). Combined model performed better than clinical model in DFS prediction in both the training set (C-index 0.815 vs 0.709; p = 0.024) and the validation set (C-index 0.866 vs 0.719; p = 0.001).

CONCLUSION

Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making.

KEY POINTS

• Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI^+5 mm) performed best with favorable C-index values. • Combined model incorporating multiparametric MRI-based radiomics with clinical risk factors performed significantly better in DFS prediction than the clinical model. • Combined model presented as a nomogram can be easily used to predict survival, thereby facilitating clinical decision-making.

Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma

OBJECTIVES

To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC).

MATERIALS AND METHODS

This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM.

RESULTS

MRI-determined depth of invasion and ADC_entropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC_10th. ADC_uniformity and wash-in_skewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC_10th, ADC_uniformity, and wash-in_skewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82.

CONCLUSIONS

Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC.

KEY POINTS

• Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC_10th, ADC_uniformity, and wash-in_skewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.

Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma

RATIONALE AND OBJECTIVES

Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC.

MATERIALS AND METHODS

One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm²). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis.

RESULTS

Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively.

CONCLUSION

T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.

Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer

OBJECTIVES

Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer.

METHODS

This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan-Meier curves were used to compare the survival difference between the high- and low-risk groups.

RESULTS

Six textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572-0.834) and 0.700 (95% CI: 0.526-0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707-0.880) and 0.754 (95% CI: 0.623-0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738-0.922) and 0.772 (95% CI: 0.615-0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05).

CONCLUSION

Radiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.

Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI

OBJECTIVE

To establish a radiomics nomogram based on relaxation maps for predicting the extramural venous invasion (EMVI) of rectal cancer (RC) and compare the diagnostic efficacy of the nomogram and subjective assessment by radiologists.

MATERIAL AND METHODS

Among 94 RC patients receiving direct surgical resection, 65 were randomly allocated to the training cohort and 29 to the validation cohort. Radiomics features were extracted from synthetic magnetic resonance imaging including T1, T2, and proton density (PD) maps. The least absolute shrinkage and selection operator methods were used for dimension reduction, feature selection, and radiomics model building. Multivariable logistic regression analysis was used for nomogram development. The performance of the nomogram was assessed with respect to its calibration, receiver operating characteristics (ROC) curve, and decision curve analysis.

RESULTS

The radiomics model demonstrated good predictive efficacy for EMVI, with an area under the ROC curve (AUC), sensitivity, and specificity of 0.912 (95% confidence interval (CI), 0.837-0.986), 0.824, and 0.875 in the training cohort and 0.877 (95% CI 0.751-1.000), 0.833, and 0.826 in the validation cohort. The nomogram had good diagnostic performance, with AUCs of 0.925 (95% CI 0.862-0.988) and 0.899 (95% CI 0.782-1.000) in the training and validation cohort. Furthermore, the radiomics signature showed better diagnostic efficiency than the subjective assessment by both readers (AUC =0.912 vs. 0.732 and 0.763, P = 0.023 and 0.028, respectively).

CONCLUSION

A radiomics nomogram was developed to preoperatively predict EMVI in RC patients. The application of the radiomics model based on relaxation maps could improve the diagnostic efficacy of EMVI.

Preoperative magnetic resonance imaging criteria for predicting lymph node metastasis in patients with stage IB1-IIA2 cervical cancer

Objective

This study aimed to identify patients with stage IB1‐IIA2 cervical cancer at low risk for lymph node metastasis (LNM) using preoperative magnetic resonance imaging (MRI) parameters.

Methods

Clinical and MRI data of patients with stage IB1‐IIA2 cervical cancer who underwent radical surgery between 2010 and 2015 were retrospectively reviewed. Clinical stage IB1‐IIA2 cervical cancer was diagnosed according to the 2009 International Federation of Gynecology and Obstetrics staging system. The low‐risk criteria for LNM were identified using logistic regression analysis. The performance of the logistic regression analysis was estimated through receiver operating characteristic curve analysis.

Results

Of 453 patients, 105 (23.2%) exhibited pathological LNM (p‐LNM). The maximal tumor diameter (adjusted odds ratio [aOR], 1.586; 95% confidence interval [CI], 1.312–1.916; p < 0.001) and LNM (aOR, 2.384; 95% CI, 1.418–4.007; p = 0.001) on preoperative MRI (m‐LNM) were identified as independent risk factors for p‐LNM using a multivariate logistic analysis. The p‐LNM rate was 4.0% for low‐risk patients (n = 124) identified using the current criteria (maximal tumor diameter <3.0 cm and no sign of m‐LNM). The 5‐year disease‐free survival rate of low‐risk patients was significantly greater than the rate of patients with a maximal tumor diameter ˃3.0 cm and/or signs of m‐LNM (90.4% vs. 82.1%; p = 0.033).

Conclusions

The low‐risk criteria for p‐LNM were a maximal tumor diameter <3.0 cm and no sign of m‐LNM. Patients with stage IB1‐IIA2 cervical cancer at low risk for m‐LNM could be candidates for radical surgery; hence, they have a lesser need for adjuvant chemoradiotherapy, thus avoiding the severe comorbidities it causes.

Radiomics in cervical and endometrial cancer

Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.

Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Background

Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors.

Methods

In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis.

Results

The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001).

Conclusions

The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

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The metastatic status of lymph nodes in cervical cancer patients can be predicted.

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Computed tomography-based radiomic model can identify the status of the normal-sized lymph node singly.

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The model may help doctors to make staging and clinical decision, and realize individualized treatment.

Purpose

Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer.

Methods

A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC).

Results

Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800.

Conclusion

We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

Region-specific Risk Factors for Pelvic Lymph Node Metastasis in Patients with Stage IB1 Cervical Cancer

Objectives: We aimed to identify the risk factors associated with pelvic lymph node metastasis (LNM) at each anatomic location in patients with stage IB1 cervical cancer.

Methods: A primary cohort of 728 patients with stage IB1 cervical cancer who underwent radical hysterectomy and systematic pelvic lymphadenectomy were retrospectively studied. All removed pelvic nodes (N=20,134) were pathologically examined. The risk factors for LNM in different anatomic regions (obturator, internal iliac, external iliac, and common iliac) were evaluated by multivariate logistic regression analyses. Nomograms were generated from the primary cohort and validated in another external cohort (N=242). The performance of the nomogram was assessed by its calibration and discrimination. Overall survival and progression-free survival in patients with different LNM patterns were compared.

Results: LNM was found in 266 (1.3%) removed nodes and 106 (14.6%) patients. The incidences of LNM at the obturator, internal iliac, external iliac, common iliac, and parametrial regions were 8.5%, 5.4%, 4.7%, 1.9% and 1.8%, respectively. Among others, tumour size and lymph-vascular space invasion (LVSI), which are preoperatively assessable, were identified as independent risk factors of LNM in the common iliac region and the lower pelvis, respectively, and age was an additional independent risk factor of obturator LNM. The negative predictive values of tumour size <2 cm for common iliac LNM and negative LVSI combined with older age (> 50 years) for obturator LNM were 100% and 98.7%, respectively. A nomogram of these two factors showed good calibration and discrimination (concordance index, 0.761 in the primary cohort and 0.830 in validation cohort). The patients with common iliac LNM had poorer survival than those with LNM confined to the lower pelvis, while the differences in survival between patients with LNM confined to one node, one region or single side and those with more widely spreading LNM were not statistically significant.

Conclusions: Tumour size, LVSI and age are region-specific risk factors for pelvic LNM in IB1 cervical cancer, which could be used to allocate the appropriate extent of pelvic lymphadenectomy.

Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients

OBJECTIVE

To investigate the feasibility of T₂WI-based radiomics nomogram analysis to non-invasively predict normal-sized pelvic lymph node (LN) metastasis (LNM) in cervical cancer patients.

METHODS

Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T₂WI (model 1), model 1 combined with LN radiomics features (model 2), and model 2 combined with tumor score (model 3). Diagnostic performance was assessed and compared.

RESULTS

Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15).

CONCLUSIONS

T₂WI-based radiomics nomogram incorporating the LN radiomics signature with the clinical morphological LN features is promising for predicting the normal-sized pelvic LNM in cervical cancer patients. The original tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

KEY POINTS

• The combination of LN radiomics signature with LN clinical morphological features on T₂WI could discriminate LNM relatively well. • The tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer

PURPOSE

To investigate the predictive value of MRI-based radiomics features for lymph node metastasis (LNM) and vascular endothelial growth factor (VEGF) expression in patients with cervical cancer.

METHOD

A total of 163 patients with cervical cancer were enrolled in this study. A total of 134 patients were included for LNM differentiation, and 118 were included for VEGF expression discrimination. The patients were randomly assigned to the training group or test group at a ratio of 2:1. Radiomics features were extracted from T1WI enhanced and T2WI MRI scans of each patient, and tumor stage was also documented according to the International Federation of Gynecology and Obstetrics (FIGO) guidelines. The least absolute shrinkage and selection operator algorithm was used for feature selection. The results of 5-fold cross validation were applied to select the best classification models. The performances of the constructed models were further evaluated with the test group.

RESULTS

Sixteen radiomics features and the FIGO stage were selected to construct the LNM discrimination model. The LNM prediction model achieved the best diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.95 and 0.88 in the training group and test group, respectively. Nine radiomics characteristics were screened to build the VEGF prediction model, with AUCs of 0.82 and 0.70 in the training group and test group, respectively. Decision curve analysis confirmed their clinical usefulness.

CONCLUSIONS

The presented radiomics prediction models demonstrated potential to noninvasively differentiate LNM and VEGF expression in cervical cancer.

A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma

OBJECTIVES

To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC).

METHODS

Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness.

RESULTS

Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts.

CONCLUSION

The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone.

ADVANCES IN KNOWLEDGE

A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.

Preoperative Assessment for High-Risk Endometrial Cancer by Developing an MRI- and Clinical-Based Radiomics Nomogram: A Multicenter Study

BACKGROUND

High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately.

PURPOSE

To develop a radiomics nomogram for high-risk EC prediction preoperatively.

STUDY TYPE

Retrospective.

POPULATION

In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E).

FIELD STRENGTH/SEQUENCE

1.5/3T scanners; T₂ -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences.

ASSESSMENT

A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI).

STATISTICAL TESTS

Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters.

RESULTS

The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively.

DATA CONCLUSION

The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.

Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer

OBJECTIVE

To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer.

METHODS

216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts.

RESULTS

95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts).

CONCLUSION

The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making.

ADVANCES IN KNOWLEDGE

ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.