Radiomics: Principles and radiotherapy applications

Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

Predicting vertebral compression fracture prior to spinal SBRT using radiomics from planning CT

PURPOSE

The purpose of the study was to develop a predictive model for vertebral compression fracture (VCF) prior to spinal stereotactic body radiation therapy (SBRT) using radiomics features extracted from pre-treatment planning CT images.

METHODS

A retrospective analysis was conducted on 85 patients (114 spinal lesions) who underwent spinal SBRT. Radiomics features were extracted from pre-treatment planning CT images and used to develop a predictive model using a classification algorithm selected from nine different machine learning algorithms. Four different models were trained, including clinical features only, clinical and radiomics features, radiomics and dosimetric features, and all features. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC).

RESULTS

The model that used all features (radiomics, clinical, and dosimetric) showed the highest performance with an AUC of 0.871. The radiomics and dosimetric features model had the superior performance in terms of accuracy, precision, recall, and F1-score.

CONCLUSION

The developed predictive model based on radiomics features extracted from pre-treatment planning CT images can accurately predict the likelihood of VCF prior to spinal SBRT. This model has significant implications for treatment planning and preventive measures for patients undergoing spinal SBRT. Future research can focus on improving model performance by incorporating new data and external validation using independent data sets.

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis

BACKGROUND

Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns.

METHODS

In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists.

RESULTS

Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6).

CONCLUSION

Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.

Next-level precision medicine: why the theragnostic approach is the future

Theragnostics represents one of the most innovative fields of precision medicine with a huge potential in the field of oncology in the next years. The use of a pair of selective radiopharmaceuticals for cellular receptors, used for diagnostic and therapeutic purposes (PRRT), finds applications in the Neuroendocrine tumors and metastatic Castration-Resistant prostate cancer (mCRPC) thanks, respectively, to somatostatin receptor agonists and PSMA-based peptides. Further evolutions of theragnostics will be possible to the radioimmunoconjugates used both in the diagnostic (Immuno-PET) and in the therapeutic fields (radioimmunotherapy). It is evident that in the "omics-era," theragnostics could become a necessary method, not only in order to improve our knowledge of tumor biology, but also, to find more and more targeted therapies in a multidisciplinary context and in a tailor-based approach.

Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung

Radiomic features were used in efforts to characterize radiation-induced normal tissue injury as well as identify if human embryonic stem cell (hESC) derived Extracellular Vesicle (EV) treatment could resolve certain adverse complications. A cohort of mice (n=12/group) were given whole lung irradiation (3×8Gy), local irradiation to the right lung apex (3×12Gy), or no irradiation. The hESC-derived EVs were systemically administered three times via retro-orbital injection immediately after each irradiation. Cone-Beam Computed Tomography (CBCT) images were acquired at baseline and 2 weeks after the final radiation/EV treatment. Whole lung image segmentation was performed and radiomic features were extracted with wavelet filtering applied. A total of 851 features were extracted per image and recursive feature elimination was used to refine, train and validate a series of random forest classification models. Classification models trained to identify irradiated from unirradiated animals or EV treated from vehicle-injected animals achieved high prediction accuracies (94% and 85%). In addition, radiomic features from the locally irradiated dataset showed significant radiation impact and EV sparing effects that were absent in the unirradiated left lung. Our data demonstrates that radiomics has the potential to characterize radiation-induced lung injury and identify therapeutic efficacy at early timepoints.

Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL

CONTEXT

Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL).

OBJECTIVES

We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome.

METHODS

A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves.

RESULTS

The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed.

CONCLUSION

The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

INTRODUCTION

The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered.

RESULTS

A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.

DISCUSSION

AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network

BACKGROUND

Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression.

PURPOSE

To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs).

STUDY TYPE

Prospective.

POPULATION

A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium.

FIELD STRENGTH/SEQUENCE

A 3.0 T/resting-state functional MRI using the gradient echo sequence.

ASSESSMENT

A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores.

STATISTICAL TESTS

Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant.

RESULTS

The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD.

DATA CONCLUSION

Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

A contrast-enhanced CT-based radiomic nomogram for the differential diagnosis of intravenous leiomyomatosis and uterine leiomyoma

Objective

Uterine intravenous leiomyomatosis (IVL) is a rare and unique leiomyoma that is difficult to surgery due to its ability to extend into intra- and extra-uterine vasculature. And it is difficult to differentiate from uterine leiomyoma (LM) by conventional CT scanning, which results in a large number of missed diagnoses. This study aimed to evaluate the utility of a contrast-enhanced CT-based radiomic nomogram for preoperative differentiation of IVL and LM.

Methods

124 patients (37 IVL and 87 LM) were retrospectively enrolled in the study. Radiomic features were extracted from contrast-enhanced CT before surgery. Clinical, radiomic, and combined models were developed using LightGBM (Light Gradient Boosting Machine) algorithm to differentiate IVL and LM. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).

Results

Clinical factors, such as symptoms, menopausal status, age, and selected imaging features, were found to have significant correlations with the differential diagnosis of IVL and LM. A total of 108 radiomic features were extracted from contrast-enhanced CT images and selected for analysis. 29 radiomics features were selected to establish the Rad-score. A clinical model was developed to discriminate IVL and LM (AUC=0.826). Radiomic models were used to effectively differentiate IVL and LM (AUC=0.980). This radiological nomogram combined the Rad-score with independent clinical factors showed better differentiation efficiency than the clinical model (AUC=0.985, p=0.046).

Conclusion

This study provides evidence for the utility of a radiomic nomogram integrating clinical and radiomic signatures for differentiating IVL and LM with improved diagnostic accuracy. The nomogram may be useful in clinical decision-making and provide recommendations for clinical treatment.

Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis

INTRODUCTION

Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity.

METHODS

A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included.

RESULTS

Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively.

CONCLUSION

Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.

Can radiomics be used to detect hypoxic-ischemic encephalopathy in neonates without magnetic resonance imaging abnormalities?

BACKGROUND

No study has assessed normal magnetic resonance imaging (MRI) findings to predict potential brain injury in neonates with hypoxic-ischemic encephalopathy (HIE).

OBJECTIVE

We aimed to evaluate the efficacy of MRI-based radiomics models of the basal ganglia, thalami and deep medullary veins to differentiate between HIE and the absence of MRI abnormalities in neonates.

MATERIALS AND METHODS

In this study, we included 38  full-term neonates with HIE and normal MRI findings and 89 normal neonates. Radiomics features were extracted from T1-weighted images, T2-weighted images, diffusion-weighted imaging and susceptibility-weighted imaging (SWI). The different models were evaluated using receiver operating characteristic curve analysis. Clinical utility was evaluated using decision curve analysis.

RESULTS

The SWI model exhibited the best performance among the seven single-sequence models. For the training and validation cohorts, the area under the curves (AUCs) of the SWI model were 1.00 and 0.98, respectively. The combined nomogram model incorporating SWI Rad-scores and independent predictors of clinical characteristics was not able to distinguish HIE in patients without MRI abnormalities from the control group (AUC, 1.00). A high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test. Decision curve analysis was used for the SWI, clinical and combined nomogram models. The decision curve showed that the SWI and combined nomogram models had better predictive performance than the clinical model.

CONCLUSIONS

HIE can be detected in patients without MRI abnormalities using an MRI-based radiomics model. The SWI model performed better than the other models.

Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients

BACKGROUND AND PURPOSE

This study aims to build machine learning models to predict radiation-induced rectal toxicities for three clinical endpoints and explore whether the inclusion of radiomic features calculated on radiotherapy planning computerised tomography (CT) scans combined with dosimetric features can enhance the prediction performance.

MATERIALS AND METHODS

183 patients recruited to the VoxTox study (UK-CRN-ID-13716) were included. Toxicity scores were prospectively collected after 2 years with grade ≥ 1 proctitis, haemorrhage (CTCAEv4.03); and gastrointestinal (GI) toxicity (RTOG) recorded as the endpoints of interest. The rectal wall on each slice was divided into 4 regions according to the centroid, and all slices were divided into 4 sections to calculate region-level radiomic and dosimetric features. The patients were split into a training set (75%, N = 137) and a test set (25%, N = 46). Highly correlated features were removed using four feature selection methods. Individual radiomic or dosimetric or combined (radiomic + dosimetric) features were subsequently classified using three machine learning classifiers to explore their association with these radiation-induced rectal toxicities.

RESULTS

The test set area under the curve (AUC) values were 0.549, 0.741 and 0.669 for proctitis, haemorrhage and GI toxicity prediction using radiomic combined with dosimetric features. The AUC value reached 0.747 for the ensembled radiomic-dosimetric model for haemorrhage.

CONCLUSIONS

Our preliminary results show that region-level pre-treatment planning CT radiomic features have the potential to predict radiation-induced rectal toxicities for prostate cancer. Moreover, when combined with region-level dosimetric features and using ensemble learning, the model prediction performance slightly improved.

Radiomics based of deep medullary veins on susceptibility-weighted imaging in infants: predicting the severity of brain injury of neonates with perinatal asphyxia

OBJECTIVE

This study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates.

METHODS

A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interest (ROI). For the purpose of identifying optimal radiomics features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomics features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility.

RESULTS

A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomics model and the combined nomogram model was 0.84 and 0.84. For the validation cohort, the AUC of the radiomics model and the combined nomogram model was 0.80 and 0.79, respectively. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group.

CONCLUSION

We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.

Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma

AIMS

To use pre-treatment magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients with stage T4/N0-3/M0 within 5 years after radiotherapy.

MATERIALS AND METHODS

This study retrospectively examined 98 patients (198 temporal lobes) with stage T4/N0-3/M0 NPC. Participants were enrolled into a training cohort or a validation cohort in a ratio of 7:3. Radiomics features were extracted from pre-treatment magnetic resonance imaging that were T1-and T2-weighted. Spearman rank correlation, the t-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select significant radiomics features; machine-learning models were used to generate radiomics signatures (Rad-Scores). Rad-Scores and clinical factors were integrated into a nomogram for prediction of RTLI. Nomogram discrimination was evaluated using receiver operating characteristic analysis and clinical benefits were evaluated using decision curve analysis.

RESULTS

Participants were enrolled into a training cohort (n = 139) or a validation cohort (n = 59). In total, 3568 radiomics features were initially extracted from T1-and T2-weighted images. Age, D_max, D_1cc and 16 stable radiomics features (six from T1-weighted and 10 from T2-weighted images) were identified as independent predictive factors. A greater Rad-Score was associated with a greater risk of RTLI. The nomogram showed good discrimination, with a C-index of 0.85 (95% confidence interval 0.79-0.92) in the training cohort and 0.82 (95% confidence interval 0.71-0.92) in the validation cohort.

CONCLUSION

We developed models for the prediction of RTLI in patients with stage T4/N0-3/M0 NPC using pre-treatment radiomics data and clinical data. Nomograms from these pre-treatment data improved the prediction of RTLI. These results may allow the selection of patients for earlier clinical interventions.

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer

Objectives

To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features.

Methods

This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively.

Results

In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses.

Conclusions

The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.

Covid-19 and radiotherapy: a systematic review after 2 years of pandemic

Introduction

Following the Covid-19 pandemic spread, changes in clinical practice were necessary to limit the pandemic diffusion. Also, oncological practice has undergone changes with radiotherapy (RT) treatments playing a key role.Although several experiences have been published, the aim of this review is to summarize the current evidence after 2 years of pandemic to provide useful conclusions for clinicians.

Methods

A Pubmed/MEDLINE and Embase systematic review was conducted. The search strategy was "Covid AND Radiotherapy" and only original articles in the English language were considered.

Results

A total of 2.733 papers were obtained using the mentioned search strategy. After the complete selection process, a total of 281 papers were considered eligible for the analysis of the results.

Discussion

RT has played a key role in Covid-19 pandemic as it has proved more resilient than surgery and chemotherapy. The impact of the accelerated use of hypofractionated RT and telemedicine will make these strategies central also in the post-pandemic period.

Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy

Background

Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT).

Materials and Methods

Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability.

Results

For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively).

Conclusions

The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change.

Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics

This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma

Background

Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.

Methods

A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.

Results

The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.

Conclusion

The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.

A Radiomic Approach to Access Tumor Immune Status by CD8+TRMs on Surgically Resected Non-Small-Cell Lung Cancer

Purpose

Immunotherapy has made breakthroughs in the treatment of non-small-cell lung cancer (NSCLC); however, only a subset of patients achieved long-term survival, so it is of great importance to find a biomarker of lung cancer thus guide immunotherapy. Studies have shown that the infiltration level of tissue resident memory CD8⁺ T cells (CD8⁺ TRMs) is positively correlated with lung cancer prognosis and can be an ideal biomarker for assessing the tumor local immune status. We screened the radiomic features associated with CD8⁺ TRMs as targets in NSCLC surgical specimens by radiomic approaches, and established a radiomic predictive model to assess the local immune status, which may provide a scientific reference for lung cancer treatment strategies.

Patients and Methods

We retrospectively analyzed the NSCLC surgical specimens immune cell database and extracted CD8⁺ TRMs cell data, preoperative CT scan data were achieved. A total of 97 patients containing complete preoperative data were included, radiomic features were extracted from the preoperative CT image data. All the patients were divided into two groups, namely high-CD8⁺ TRMs infiltrated group and low-CD8⁺ TRMs infiltrated group, based on the proportion of CD8⁺ TRMs cells subset in the immune cell population. The most valuable radiomic features and semantic features were extracted and selected, and a neural network model was established to predict the level of CD8⁺ TRMs cell infiltration level to assess the tumor local immune status.

Results

The NSCLC tumor immune status predictive model was built to discriminate high- from low-CD8⁺ TRMs with an area under the curve (AUC) of 0.788 (95% CI) in the training set and 0.753 (95% CI) in the validation set.

Conclusion

The radiomic models using CT image data showed a good predictive performance for accessing NSCLC immune status thus has great potential for personalized therapeutic decision making.

Radiomics and Digital Image Texture Analysis in Oncology (Review)

One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology.

The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.

Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

Radiomics in Head and Neck Cancers Radiotherapy. Promises and Challenges

Radiomics, a subdomain of artificial intelligence, consists in extracting a large volume of data from all medical imaging techniques and correlating them with clinical data in order to build predictive and prognostic models. Radiomics is related to radiogenomics that correlates genetic mutations and molecular and biological characteristics of tissues with information extracted from medical imaging. Both are state-of-the-art fields of translational biomedical research. The ability to predict early patient survival and response to treatment, but also the capacity to identify tumor subtypes non-invasively, could make radiomics a key player with an essential role in personalized oncology. In head and neck cancer radiotherapy, radiomic algorithms can predict not only the response to radiochemotherapy or induction chemotherapy but also the need for planning through adaptive radiotherapy (ART). Radiomics can also predict the risk of severe toxicities, especially that of xerostomia. Given the benefit that a de-escalation of treatment can bring in selected cases to improve the quality of life, radiomics is expected to be part of the therapeutic decision for head and neck cancers in the near future, and may help identify cases where de-escalation of multimodal therapy will not jeopardize the therapeutic benefit.

Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

OBJECTIVES

Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC.

METHODS

A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves.

RESULTS

The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value.

CONCLUSIONS

We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC.

KEY POINTS

• There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

Objectives

To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics.

Materials and Methods

Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated.

Results

Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024).

Conclusions

DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test–retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC > 0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.

Radiomic features for prostate cancer grade detection through formal verification

AIM

Prostate cancer represents the most common cancer afflicting men. It may be asymptomatic at the early stage. In this paper, we propose a methodology aimed to detect the prostate cancer grade by computing non-invasive shape-based radiomic features directly from magnetic resonance images.

MATERIALS AND METHODS

We use a freely available dataset composed by coronal magnetic resonance images belonging to 112 patients. We represent magnetic resonance slices in terms of formal model, and we exploit model checking to check whether a set of properties (formulated with the support of pathologists and radiologists) is verified on the formal model. Each property is related to a different cancer grade with the aim to cover all the cancer grade groups.

RESULTS

An average specificity equal to 0.97 and an average sensitivity equal to 1 have been obtained with our methodology.

CONCLUSION

The experimental analysis demonstrates the effectiveness of radiomics and formal verification for Gleason grade group detection from magnetic resonance.

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study

PURPOSE

The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

METHODS

The study population consisted of 31 prostate cancer patients. Radiomics features were extracted from weekly CBCT scans performed for verifying treatment position. From the data, logistic-regression models were learned for establishing tumor stage, Gleason score, level of prostate-specific antigen, and risk stratification, and for predicting biochemical recurrence. Performance of the learned models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) or the area under the precision-recall curve (AUC-PRC).

RESULTS

Results suggest that the histogram-based Energy and Kurtosis features and the shape-based feature representing the standard deviation of the maximum diameter of the prostate gland during treatment are predictive of biochemical relapse and indicative of patients at high risk.

CONCLUSION

Our results suggest the usefulness of CBCT-based radiomics for treatment definition in prostate cancer.

Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection

BACKGROUND

This study aimed to evaluate the efficiency of gadoxetic acid-enhanced MRI-based radiomics features for prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients after surgical resection.

METHODS

This prospective study approved by the Institutional Review Board enrolled 120 patients with pathologically confirmed HCC. Radiomics signatures (rad-scores) were built from radiomics features in 3 different regions of interest (ROIs) with the least absolute shrinkage and selection operator (LASSO) cox regression analysis. Preoperative clinical characteristics and semantic imaging features potentially associated with patient survival were evaluated to develop a clinic-radiological model. The radiomics features and clinic-radiological predictors were integrated into a joint model using multivariable Cox regression analysis. Kaplan-Meier analysis and log-rank tests were performed to compare the discriminative performance and evaluated on the validation cohort.

RESULTS

The radiomics signatures showed a significant association with patient survival in both cohorts (all P<0.001). The BCLC (Barcelona clinic liver cancer) stage, non-smooth tumor margin, and the combined rad-score were independently associated with OS. Moreover, the combined model incorporating with clinic-radiological and radiomics features showed an improved predictive performance with C-index of 0.92 [95% confidence interval (CI): 0.87-0.97], compared to the clinic-radiological model (C-index, 0.86, 95% CI: 0.79-0.94; P=0.039) or the combined rad-score (C-index, 0.88, 95% CI: 0.81-0.95; P=0.016).

CONCLUSIONS

Radiomics features along with clinic-radiological predictors can efficiently aid in preoperative HCC prognosis prediction after surgical resection and enable a step forward precise medicine.

Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy

Purpose

Imaging in pancreatic cancer is a challenge, especially regarding therapy response evaluation. Tumor size, attenuation, and perfusion are widely used as parameters for computed tomography (CT) examinations, but are often limited due to blurry tumor borders and missing qualitative parameters. To improve monitoring of therapy response, we tested a new CT-based approach of tumor heterogeneity feature analysis.

Methods

A total of 13 patients with pancreatic adenocarcinoma undergoing abdominal CT according to standard as baseline imaging with clinical follow-up and imaging (median time span 64 days) under systematic therapy (FOLFIRINOX/gemcitabine) were retrospectively analyzed. Progression was defined as new lesions and local tumor spread. Tumor heterogeneity analysis was performed using mintLesion®. Seven different image features referring to image heterogeneity were analyzed. Statistical analysis was performed with Spearman's rank correlation and Mann-Whitney U test.

Results

During follow-up, tumor volume did not significantly change between our groups with overall progression (local and systemic) and progression-free patients (p = 0.661). Mean positivity of pixel values were significantly higher in patients without progression compared to patients with progression (p = 0.030). There was a significant negative correlation between changes in kurtosis and time to local tumor spread (p = 0.008) or systemic progression (p = 0.017).

Conclusions

Results suggest that analysis of tumor heterogeneity might provide valuable information from routine-acquired images regarding therapy response evaluation. This might help adjusting therapy regimes and could be easily integrated in clinical workflows. Furthermore, this procedure might possibly predict therapy response and, hence could lead the way to find a potential marker for progression-free survival.

MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Machine learning helps identifying volume-confounding effects in radiomics

PURPOSE

Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development.

Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers

Prostate cancer is a significant public health burden and a major cause of morbidity and mortality among men worldwide. Only in 2018 were reported 1.3 million of new diagnosed patients. Usually an invasive trans-perineal biopsy is the way to diagnose prostate cancer grade by prostate tissue removal. In this paper we propose a non invasive method to detect the prostate cancer grade (the so-called Gleason score) by computing radiomic biomarkers from magnetic resonance images. Furthermore, the proposed method predicts whether the cancer is suitable for the surgery treatment basing on the pathologist and surgeon suggestions. We represent patient magnetic resonances in terms of formal models and, through an algorithm designed by authors, we infer a set of properties aimed to predict the Gleason score and the treatment. By exploiting a formal verification environment, the properties are verified on two different real-world data-sets, the first one is composed of 36 patients, while the second one of 26, confirming the effectiveness of the proposed method.