Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

Multimodal Ultrasound Radiomic Technology for Diagnosing Benign and Malignant Thyroid Nodules of Ti-Rads 4-5: A Multicenter Study

This study included 468 patients and aimed to use multimodal ultrasound radiomic technology to predict the malignancy of TI-RADS 4-5 thyroid nodules. First, radiomic features are extracted from conventional two-dimensional ultrasound (transverse ultrasound and longitudinal ultrasound), strain elastography (SE), and shear-wave-imaging (SWE) images. Next, the least absolute shrinkage and selection operator (LASSO) is used to screen out features related to malignant tumors. Finally, a support vector machine (SVM) is used to predict the malignancy of thyroid nodules. The Shapley additive explanation (SHAP) method was used to intuitively analyze the specific contributions of radiomic features to the model's prediction. Our proposed model has AUCs of 0.971 and 0.856 in the training and testing sets, respectively. Our proposed model has a higher prediction accuracy compared to those of models with other modal combinations. In the external validation set, the AUC of the model is 0.779, which proves that the model has good generalization ability. Moreover, SHAP analysis was used to examine the overall impacts of various radiomic features on model predictions and local explanations for individual patient evaluations. Our proposed multimodal ultrasound radiomic model can effectively integrate different data collected using multiple ultrasound sensors and has good diagnostic performance for TI-RADS 4-5 thyroid nodules.

Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics

Background

Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion.

Methods

This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve.

Results

In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male (n = 63) than female (n = 19) patients in the LVI (+) group. Diameter and differentiation were the independent risk factors for determining LVI (-) and (+). A combined model was found to be relatively highly discriminative based on the area under the ROC curve for both the training (0.853, 95% CI: 0.784-0.920, sensitivity: 0.650 and specificity: 0.907) and the validation cohorts (0.742, 95% CI: 0.559-0.925, sensitivity: 0.736 and specificity: 0.700).

Conclusions

The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature

OBJECTIVES

To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis.

RESULTS

Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC.

CLINICAL RELEVANCE STATEMENT

Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making.

KEY POINTS

• Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer

PURPOSE

Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS).

MATERIALS AND METHODS

A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs.

RESULTS

A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77).

CONCLUSIONS

Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

BACKGROUND

Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors.

PURPOSE

To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC).

MATERIAL AND METHODS

In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed.

RESULTS

The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram.

CONCLUSION

CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer

Background

Hepatocellular cancer (HCC) is one of the most common tumors worldwide, and Ki-67 is highly important in the assessment of HCC. Our study aimed to evaluate the value of ultrasound radiomics based on intratumoral and peritumoral tissues in predicting Ki-67 expression levels in patients with HCC.

Methods

We conducted a retrospective analysis of ultrasonic and clinical data from 118 patients diagnosed with HCC through histopathological examination of surgical specimens in our hospital between September 2019 and January 2023. Radiomics features were extracted from ultrasound images of both intratumoral and peritumoral regions. To select the optimal features, we utilized the t-test and the least absolute shrinkage and selection operator (LASSO). We compared the area under the curve (AUC) values to determine the most effective modeling method. Subsequently, we developed four models: the intratumoral model, the peritumoral model, combined model #1, and combined model #2.

Results

Of the 118 patients, 64 were confirmed to have high Ki-67 expression while 54 were confirmed to have low Ki-67 expression. The AUC of the intratumoral model was 0.796 (0.649-0.942), and the AUC of the peritumoral model was 0.772 (0.619-0.926). Furthermore, combined model#1 yielded an AUC of 0.870 (0.751-0.989), and the AUC of combined model#2 was 0.762 (0.605-0.918). Among these models, combined model#1 showed the best performance in terms of AUC, accuracy, F1-score, and decision curve analysis (DCA).

Conclusion

We presented an ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information to accurately predict Ki-67 expression in HCC patients. We believe that incorporating both regions in a proper manner can enhance the diagnostic performance of the prediction model. Nevertheless, it is not sufficient to include both regions in the region of interest (ROI) without careful consideration.

Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from ¹⁸F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent ¹⁸F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTV_TOT) and whole-body Total Lesion Glycolysis (TLG_WB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTV_TOT, TLG_WB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTV_TOT (p = 0.0050), TLG_WB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTV_TOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTV_TOT ≤ 89.5 mL had higher OS than those with MTV_TOT > 89.5 mL (p = 0.0063). Combining CoV and MTV_TOT, patients with CoV ≤ 0.38 and MTV_TOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Background

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules.

Methods

Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured.

Results

Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features.

Conclusions

RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy

PURPOSE

Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART.

METHODS

A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve.

RESULTS

Spearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2-3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98.

CONCLUSIONS

A machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.

MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma

Objective

To investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC).

Methods

A total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis.

Results

The nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness.

Conclusions

The MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.

Synthetic MRI improves radiomics-based glioblastoma survival prediction

Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and ≤ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.

Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors

Background

To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs).

Methods

A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses.

Results

Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set.

Conclusions

CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.

Development and Validation of a Nomogram Based on 18F-FDG PET/CT Radiomics to Predict the Overall Survival in Adult Hemophagocytic Lymphohistiocytosis

Purpose: Hemophagocytic lymphohistiocytosis (HLH) is a rare and severe disease with a poor prognosis. We aimed to determine if ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT-derived radiomic features alone or combination with clinical parameters could predict survival in adult HLH.

Methods: This study included 70 adults with HLH (training cohort, n = 50; validation cohort, n = 20) who underwent pretherapeutic ¹⁸F-FDG PET/CT scans between August 2016 and June 2020. Radiomic features were extracted from the liver and spleen on CT and PET images. For evaluation of 6-month survival, the features exhibiting p < 0.1 in the univariate analysis between non-survivors and survivors were selected. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a radiomics score (Rad-score). A nomogram was built by the multivariate regression analysis to visualize the predictive model for 3-month, 6-month, and 1-year survival, while the performance and usefulness of the model were evaluated by calibration curves, the receiver operating characteristic (ROC) curves, and decision curves.

Results: The Rad-score was able to predict 6-month survival in adult HLH, with area under the ROC curves (AUCs) of 0.927 (95% CI: 0.878–0.974) and 0.869 (95% CI: 0.697–1.000) in the training and validation cohorts, respectively. The radiomics nomogram combining the Rad-score with the clinical parameters resulted in better performance for predicting 6-month survival than the clinical model or the Rad-score alone. Moreover, the nomogram displayed superior discrimination, calibration, and clinical usefulness in both the cohorts.

Conclusion: The newly developed Rad-score is a powerful predictor for overall survival (OS) in adults with HLH. The nomogram has great potential for predicting 3-month, 6-month, and 1-year survival, which may timely guide personalized treatments for adult HLH.

Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

Simple Summary

Low-grade gliomas (LGG) with the 1p/19q co-deletion mutation have been proven to have a better survival prognosis and response to treatment than individuals without the mutation. Identifying this mutation has a vital role in managing LGG patients; however, the current diagnostic gold standard, including the brain-tissue biopsy or the surgical resection of the tumor, remains highly invasive and time-consuming. We proposed a model based on the eXtreme Gradient Boosting (XGBoost) classifier to detect 1p/19q co-deletion mutation using non-invasive medical images. The performance of our model achieved 87% and 82.8% accuracy on the training and external test set, respectively. Significantly, the prediction was based on only seven optimal wavelet radiomics features extracted from brain Magnetic Resonance (MR) images. We believe that this model can address clinicians in the rapid diagnosis of clinical 1p/19q co-deletion mutation, thereby improving the treatment prognosis of LGG patients.

Abstract

The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.

A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis

Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients' management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics features examined at different scales were extracted by visual descriptors such as local binary patterns, wavelet transforms and Gabor filters. An exhaustive evaluation involving a variety of wavelet families and parameters was performed in order to shed light on the impact of scale on pathomics based CRC tissue differentiation. Our model achieved a performance accuracy of 95.3% with tenfold cross validation demonstrating superior performance compared to 87.4% reported in recent studies. Furthermore, we experimentally showed that the first and the second levels of the wavelet approximations can be used without compromising classification performance.

The Biological Meaning of Radiomic Features

An earlier incorrect version appeared online. This article was corrected on February 10, 2021.

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades

Purpose

To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.

Materials and Methods

A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.

Result

Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.

Conclusion

In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

Resistance to chemotherapy often results from dysfunctional apoptosis, however multiple proteins with overlapping functions regulate this pathway. We sought to determine whether an extensively validated, deterministic apoptosis systems model, 'DR_MOMP', could be used as a stratification tool for the apoptosis sensitiser and BCL-2 antagonist, ABT-199 in patient-derived xenograft (PDX) models of colorectal cancer (CRC). Through quantitative profiling of BCL-2 family proteins, we identified two PDX models which were predicted by DR_MOMP to be sufficiently sensitive to 5-fluorouracil (5-FU)-based chemotherapy (CRC0344), or less responsive to chemotherapy but sensitised by ABT-199 (CRC0076). Treatment with ABT-199 significantly improved responses of CRC0076 PDXs to 5-FU-based chemotherapy, but showed no sensitisation in CRC0344 PDXs, as predicted from systems modelling. ¹⁸F-Fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) scans were performed to investigate possible early biomarkers of response. In CRC0076, a significant post-treatment decrease in mean standard uptake value was indeed evident only in the combination treatment group. Radiomic CT feature analysis of pre-treatment images in CRC0076 and CRC0344 PDXs identified features which could phenotypically discriminate between models, but were not predictive of treatment responses. Collectively our data indicate that systems modelling may identify metastatic (m)CRC patients benefitting from ABT-199, and that ¹⁸F-FDG-PET could independently support such predictions.

Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians

In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Objective: Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features.

Methods: A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed.

Results: We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features.

Conclusion: The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.

Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]

Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (p = 0.045 and p = 0.032 for conventional imaging features and texture features, respectively). The integration of imaging, texture, and clinical features yielded a significant improvement in accuracy, supporting the synergistic value of these features in the predictive model. The findings suggest that the texture features, when combined with conventional imaging and clinical markers, may provide an objective, accurate, and integrated prediction of glioma grades. The proposed digital pathology imaging-based marker may help to (i) stratify patients into clinical trials, (ii) select patients for targeted therapies, and (iii) personalize treatment planning on an individual person basis.

Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer

Purpose: To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types.

Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings.

Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set.

Conclusion: Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions

Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common. To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the malignant samples into different cancer grades. We propose a multi-step gland segmentation method, which models tissue components as ellipsoids. For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ensemble-classification method. Using two datasets (Rawalpindi Medical College (RMC), n = 174 and gland segmentation (GlaS), n = 165) with three cancer grades, our method reliably delineated gland regions (RMC = 87.5%, GlaS = 88.4%), detected the presence of malignancy (RMC = 97.6%, GlaS = 98.3%), and predicted tumor grade (RMC = 98.6%, GlaS = 98.6%). Training the model using one dataset and testing it on the other showed strong concordance in cancer detection (Train RMC – Test GlaS = 94.5%, Train GlaS – Test RMC = 93.7%) and grading (Train RMC – Test GlaS = 95%, Train GlaS – Test RMC = 95%) suggesting that the model will be applicable across institutions. With further prospective validation, the techniques demonstrated here may provide a reproducible and easily accessible method to standardize analysis of colon cancer specimens.

The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy

Immunotherapy by immune checkpoint inhibitors has emerged as an effective treatment for a slight proportion of patients with aggressive tumors. Currently, some molecular determinants, such as the expression of the programmed cell death ligand-1 (PD-L1) or the tumor mutational burden (TMB) have been used in the clinical practice as predictive biomarkers, although they fail in consistency, applicability, or reliability to precisely identify the responding patients mainly because of their spatial intratumoral heterogeneity. Therefore, new biomarkers for early prediction of patient response to immunotherapy, that could integrate several approaches, are eagerly sought. Novel methods of quantitative image analysis (such as radiomics or pathomics) might offer a comprehensive approach providing spatial and temporal information from macroscopic imaging features potentially predictive of underlying molecular drivers, tumor-immune microenvironment, tumor-related prognosis, and clinical outcome (in terms of response or toxicity) following immunotherapy. Preliminary results from radiomics and pathomics analysis have demonstrated their ability to correlate image features with PD-L1 tumor expression, high CD3 cell infiltration or CD8 cell expression, or to produce an image signature concordant with gene expression. Furthermore, the predictive power of radiomics and pathomics can be improved by combining information from other modalities, such as blood values or molecular features, leading to increase the accuracy of these models. Thus, “digital biopsy,” which could be defined by non-invasive and non-consuming digital techniques provided by radiomics and pathomics, may have the potential to allow for personalized approach for cancer patients treated with immunotherapy.

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions

Background

To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions.

Methods

A retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans. The lesion areas were delineated, and a total of 1,029 radiomics features were extracted. The least absolute shrinkage and selection operator (Lasso) algorithm method was used to select the radiomics features significantly associated with discrimination of high-risk from low-risk lesions in the anterior mediastinum. Then, 8-fold and 3-fold cross-validation logistic regression (LR) models were taken as the feature selection classifiers to build the radiomics models for UECT and CECT scan respectively. The predictive performance of the radiomics features was evaluated based on the receiver operating characteristics (ROC) curve.

Results

Each of the two radiomics classifiers included the optimal 12 radiomic features. In terms of the area under ROC curve, using the radiomics model in discriminating high-risk lesions from the low-risks, CECT images accounted for 74.1% with a sensitivity of 66.67% and specificity of 64.81%. Meanwhile, UECT images were 84.2% with a sensitivity of 71.43% and specificity of 74.07%.

Conclusions

The association of the two proposed CT-based radiomics features with the discrimination of high and low-risk lesions in anterior mediastinum was confirmed, and the radiomics features of the UECT scan were proven to have better prediction performance than the CECT's in risk grading.

On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures

Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy–spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy–spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.

Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis

Purpose: Use of quantitative imaging features and encoding the intra-tumoral heterogeneity from multi-parametric magnetic resonance imaging (mpMRI) for the prediction of Gleason score is gaining attention as a non-invasive biomarker for prostate cancer (PCa). This study tested the hypothesis that radiomic features, extracted from mpMRI, could predict the Gleason score pattern of patients with PCa. Methods: This analysis included T2-weighted (T2-WI) and apparent diffusion coefficient (ADC, computed from diffusion-weighted imaging) scans of 99 PCa patients from The Cancer Imaging Archive (TCIA). A total of 41 radiomic features were calculated from a local tumor sub-volume (i.e., regions of interest) that is determined by a centroid coordinate of PCa volume, grouped based on their Gleason score patterns. Kruskal-Wallis and Spearman's rank correlation tests were used to identify features related to Gleason score groups. Random forest (RF) classifier model was used to predict Gleason score groups and identify the most important signature among the 41 radiomic features. Results: Gleason score groups could be discriminated based on zone size percentage, large zone size emphasis and zone size non-uniformity values (p < 0.05). These features also showed a significant correlation between radiomic features and Gleason score groups with a correlation value of -0.35, 0.32, 0.42 for the large zone size emphasis, zone size non-uniformity and zone size percentage, respectively (corrected p < 0.05). RF classifier model achieved an average of the area under the curves of the receiver operating characteristic (ROC) of 83.40, 72.71, and 77.35% to predict Gleason score groups (G1) = 6; 6 < (G2) < (3 + 4) and (G3) ≥ 4 + 3, respectively. Conclusion: Our results suggest that the radiomic features can be used as a non-invasive biomarker to predict the Gleason score of the PCa patients.

Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer

BACKGROUND

Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study's purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients.

METHODS

A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive's (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman's rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS.

RESULTS

Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3.

CONCLUSION

This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.