Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study

Preoperative prediction of Ki-67 expression in medullary thyroid carcinoma based on ultrasonographic features: a 10-year retrospective study

PURPOSE

To identify ultrasonographic features that help distinguish Ki-67 expression levels in patients with medullary thyroid carcinoma (MTC).

MATERIALS AND METHODS

A total of 210 patients (245 nodules) with pathological diagnosis of MTC were included in this retrospective study between January 2013 and April 2024. Based on preoperative clinical and ultrasonographic features, univariate analysis and multivariate logistic regression analysis were performed to determine the risk factors associated with Ki-67 ≥ 5 %. A prediction model was subsequently established to evaluate the differential diagnostic performance of Ki-67 by the area under the curve (AUC).

RESULTS

Among the 210 MTC patients (245 nodules), 35 patients (41 nodules) exhibited high Ki-67 expression (Ki-67 ≥ 5 %), while 175 patients (204 nodules) had low Ki-67 expression (Ki-67 < 5 %). There were no significant differences in age, sex, body mass index (BMI), preoperative calcitonin and preoperative CEA levels between the two groups (P > 0.05). Multivariate analysis of the nodules in the two groups revealed that the ultrasound features, including location in the upper or middle region, tumor size > 2.15 cm, and markedly hypoechoic were independent risk factors for high Ki-67 expression. A prediction model was established with the AUC of 0.812 (95 % CI 0.743-0.882).

CONCLUSIONS

Compared to the low Ki-67 expression group, Ki-67 ≥ 5 % group were more likely to exhibit the ultrasound characteristics of location in the upper or middle region, tumor size > 2.15 cm, and markedly hypoechoic. The prediction model demonstrated preferable diagnostic value.

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).

AIM

To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.

METHODS

In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).

CONCLUSION

CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.

MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

RATIONALE AND OBJECTIVES

Prostate cancer (PCa) is the second most common cancer among men and a leading cause of cancer-related mortalities. Radiomics has shown promising performances in the classification of PCa grade group (GG) in several studies. Here, we aimed to systematically review and meta-analyze the performance of radiomics in predicting GG in PCa.

MATERIALS AND METHODS

Adhering to PRISMA-DTA guidelines, we included studies employing magnetic resonance imaging-derived radiomics for predicting GG, with histopathologic evaluations as the reference standard. Databases searched included Web of Sciences, PubMed, Scopus, and Embase. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and METhodological RadiomICs Score (METRICS) tools were used for quality assessment. Pooled estimates for sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) were calculated. Cochran's Q and I-squared tests assessed heterogeneity, while meta-regression, subgroup analysis, and sensitivity analysis addressed potential sources. Publication bias was evaluated using Deek's funnel plot, while clinical applicability was assessed with Fagan nomograms and likelihood ratio scattergrams.

RESULTS

Data were extracted from 43 studies involving 9983 patients. Radiomics models demonstrated high accuracy in predicting GG. Patient-based analyses yielded AUCs of 0.93 for GG≥2, 0.91 for GG≥3, and 0.93 for GG≥4. Lesion-based analyses showed AUCs of 0.84 for GG≥2 and 0.89 for GG≥3. Significant heterogeneity was observed, and meta-regression identified sources of heterogeneity. Radiomics model showed moderate power to exclude and confirm the GG.

CONCLUSION

Radiomics appears to be an accurate noninvasive tool for predicting PCa GG. It improves the performance of standard diagnostic methods, enhancing clinical decision-making.

Preoperative CT radiomicsbased model for predicting Ki67 expression in clear cell renal cell carcinoma patients

OBJECTIVES

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), and developing personalized treatment strategies is crucial for improving patient prognosis. This study aims to develop and validate a preoperative computer tomography (CT) radiomics-based predictive model to estimate Ki-67 expression in ccRCC patients, thereby assisting in clinical treatment decisions and prognosis prediction.

METHODS

A retrospective analysis was conducted on 214 ccRCC patients who underwent surgical treatment at Gansu Provincial Hospital between January 2018 and November 2023. Patients were classified into high Ki-67 expression (n=123) and low Ki-67 expression (n=91) groups based on postoperative immunohistochemical staining results. The dataset was randomly divided in a 7꞉3 ratio into a training set (n=149) and a validation set (n=65). Preoperative contrast-enhanced urinary CT images and clinical data were collected. After preprocessing, 5 mm arterial-phase CT images were manually segmented layer by layer to delineate the region of interest (ROI) using ITK-SNAP 3.8 software. Radiomic features were then extracted using the FeAture Explorer (FAE) package. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) algorithm, yielding the optimal feature set. Three classification models were constructed using logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under the curve (AUC), decision curve analysis (DCA), and calibration curves were used for model evaluation.

RESULTS

A total of 107 radiomic features were extracted from 5 mm arterial-phase CT images, and twenty-one features significantly associated with Ki-67 expression were selected using the LASSO algorithm. Predictive models were developed using LR, MLP, and SVM classifiers. In the training and validation sets, the AUC values for each model were 0.904 (95% CI 0.852 to 0.956) and 0.818 (95% CI 0.710 to 0.926) for the LR model, 0.859 (95% CI 0.794 to 0.923) and 0.823 (95% CI 0.716 to 0.929) for the MLP model, and 0.917 (95% CI 0.865 to 0.969) and 0.857 (95% CI 0.760 to 0.953) for the SVM model. DCA demonstrated that all models had good clinical net benefit, while calibration curves indicated high accuracy of the predictions, supporting the robustness and reliability of the models.

CONCLUSIONS

A CT radiomics-based model for predicting Ki-67 expression in ccRCC was successfully developed. This model provides valuable guidance for treatment planning and prognostic assessment in ccRCC patients.

Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions

Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

PURPOSE

The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.

MATERIAL AND METHODS

A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.

RESULTS

The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.

CONCLUSIONS

MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.