Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation

Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis

BACKGROUND

Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores.

METHODS

We retrospectively analyzed 622 patients who underwent SBx with prostate multiparametric MRI (mpMRI) from two centers between January 2014 to June 2022. The MRI data were collected to manually segment Regions of Interest (ROI) of the tumor layer by layer. ROI reconstructions were fused to form outline of the volume of interest (VOI), which were exported and applied to subsequent extraction of radiomics features. The t-tests, Mann-Whitney U-tests and chi-squared tests were performed to evaluate the significance of features. The logistic regression was used for calculating the PCa risk score (PCS). The PCS model was trained to optimize the SBx core number, utilizing both mpMRI radiomics and clinical features.

RESULTS

The predicted number of SBx cores was determined by PCS model. Optimal core numbers of SBx for PCS subgroups 1-5 were calculated as 13, 10, 8, 6, and 6, respectively. Accuracies of predicted core numbers were high: 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1-5. Optimized SBx reduced core rate by 41.9%. Leakage rates for PCa and clinically significant PCa were 8.2% and 3.4%, respectively. The optimized SBx also demonstrated high accuracy on the validation set.

CONCLUSION

The optimization PCS model described in this study could therefore effectively reduce the number of systematic biopsy cores obtained from patients with high PCS, especially for biopsy cores far away from suspicious lesions. This method can enhance patient experience without reducing tumor detection rate.

The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review

Head and neck cancers represent a significant source of morbidity and mortality across the world. The individual genetic makeup of each tumor can help to determine the course of treatment and can help clinicians predict prognosis. Non-invasive tools to determine the genetic status of these tumors, particularly p16 (human papillomavirus (HPV)) status could prove extremely valuable to treating clinicians and surgeons. The field of radiomics is a burgeoning area of radiology practice that aims to provide quantitative biomarkers that can be derived from radiological images and could prove useful in determining p16 status non-invasively. In this review, we summarize the current evidence for the use of radiomics to determine the HPV status of head and neck tumors. .

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives

This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.

Gender Medicine in Clinical Radiology Practice

Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

Exploring Texture Analysis to Optimize Bladder Preservation in Muscle Invasive Bladder Cancer

PURPOSE

To explore if texture analysis of Muscle Invasive Bladder Cancer (MIBC) can aid in better patient selection for bladder preservation.

METHODS

Pretreatment noncontrast CT images of 41 patients of MIBC treated with bladder preservation were included. The visible tumor was contoured on all slices by a single observer. The primary endpoint was to identify texture parameters associated with disease recurrence posttreatment. The secondary endpoints included intra and interobserver variability, single and multislice analysis, and differentiating the texture features of normal bladder and tumor. For interobserver variability of bladder tumor texture features, 3 observers contoured the visible tumor on all slices independently. Observer 1 contoured again at an interval of 1 month for intraobserver variability.

RESULTS

The median follow-up was 30 months with 12 patients having a recurrence. In the primary endpoint analysis, the mean of the pixels at Spatial Scaling Filter (SSF) 2 for the no recurrence group and recurrence group was 6.44 v 13.73 respectively (P = .031) and the same at SSF-3 was 11.95 and 22.32 respectively (P = .034). The texture features that could significantly differentiate tumor and normal bladder were mean, standard deviation and kurtosis of the pixels at SSF-2 and entropy and kurtosis of the pixels at SSF-3. Overall, there was an excellent intra and interobserver concordance in texture features. Only multislice analysis and not single-slice could differentiate recurrence and no recurrence posttreatment.

CONCLUSIONS

Texture analysis can be explored as a modality for patient selection for bladder preservation along with the established clinical parameters to improve outcomes.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis

Purpose

The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models.

Methods

We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I² values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity.

Results

We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score.

Conclusions

Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy

OBJECTIVE

Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery.

METHODS

Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis.

RESULTS

Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively.

CONCLUSION

PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.

Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery

Simple Summary

The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery.

Abstract

We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.

Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer

RATIONALE AND OBJECTIVES

To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models.

MATERIALS AND METHODS

This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient.

RESULTS

The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively).

CONCLUSION

The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.

Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer

PURPOSE

Aim of the study was to perform CT texture analysis in patients with gastric cancer (GC) to investigate potential role of radiomics for predicting the occurrence of peritoneal metastases (PM).

MATERIALS AND METHODS

In this single-centre retrospective study, patients with gastric adenocarcinoma and surgically confirmed presence or absence of PM were, respectively, enrolled in group PM and group non-PM. Patients with T1-staging, previous treatment or presence of imaging artifacts were excluded from the study. Pre-operative CT examinations were evaluated. Acquisition protocol consisted of gastric distension with water, pre-contrast and arterial phases on upper abdomen and portal phase on thorax and whole abdomen. Texture analysis was performed on portal phase images: the region of interest was manually drawn along the margins of the primitive lesion on each slice and the volume of interest of the whole tumour was obtained. A total of 38 texture parameters were extracted and analysed. ROC curves were performed on significant texture features (p < 0.05). Multiple logistic regression was conducted on features with the best AUC to identify differentiating variables for both groups.

RESULTS

A total of 90 patients were evaluated (group PM, n = 45; group non-PM, n = 45). T2/T3 tumours were prevalent in group non-PM, T4 was significantly associated with group PM. Significant differences between the two groups were observed for 22/38 texture parameters. Volume and GLRLM_LRHGE showed the greatest AUC in ROC curve analysis (0.737 and 0.734, respectively) and were found to be independent differentiating variables of group PM in the multiple regression analysis (OR 8.44, [95% CI, 1.52-46.8] and OR 18.99 [95% CI, 84-195.31], respectively).

CONCLUSIONS

Our preliminary results suggest the potential value of CT texture analysis for predicting the risk of PM from GC, which may be helpful to stratify patients and address them to the most appropriate treatment.

The use of radiomic analysis of magnetic resonance imaging in predicting distant metastases of rectal carcinoma following surgical resection: A systematic review and meta-analysis

AIM

Estimating prognosis in rectal carcinoma (RC) is challenging, with distant recurrence (DR) occurring in up to 30% of cases. Radiomics is a novel field using diagnostic imaging to investigate the tumour heterogeneity of cancers and may have the potential to predict DR. The aim of the study was to perform a systematic review of the current literature evaluating the use of radiomics in predicting DR in patients with resected RC.

METHODS

A systematic review was performed as per PRISMA guidelines to identify studies reporting radiomic analysis of magnetic resonance imaging (MRI) to predict DR in patients diagnosed with RC. Sensitivity and specificity of radiomic analyses were included for meta-analysis.

RESULTS

A total of seven studies including 1497 patients (998 males) were included, seven, five and one of whom reported radiomics, respectively. The overall pooled rate of DR from all included studies was 17.1% (256/1497), with 15.6% (236/1497), 1.3% (19/1497) and 0.2% (3/1497) of patients having hepatic, pulmonary and peritoneal metastases. Meta-analysis demonstrated that radiomics correctly predicted DR with pooled sensitivities and specificities of MRI 0.76 (95% CI: 0.73, 0.78) and 0.85 (95% CI: 0.83, 0.88), respectively.

CONCLUSION

This systematic review suggests the benefit of radiomic analysis of preoperative MRI in identifying patients with resected RC at an increased risk of DR. Our findings warrant validation in larger prospective studies as modalities to predict DR is a significant unmet need in RC. Radiomics may allow for tailored therapeutic strategies for high-risk groups.

Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy

PURPOSE

Aim of this study is to assess the ability of contrast-enhanced CT image-based radiomic analysis to predict local response (LR) in a retrospective cohort of patients affected by pancreatic cancer and treated with stereotactic body radiation therapy (SBRT). Secondary aim is to evaluate progression free survival (PFS) and overall survival (OS) at long-term follow-up.

METHODS

Contrast-enhanced-CT images of 37 patients who underwent SBRT were analyzed. Two clinical variables (BED, CTV volume), 27 radiomic features were included. LR was used as the outcome variable to build the predictive model. The Kaplan-Meier method was used to evaluate PFS and OS.

RESULTS

Three variables were statistically correlated with the LR in the univariate analysis: Intensity Histogram (StdValue feature), Gray Level Cooccurrence Matrix (GLCM25_Correlation feature) and Neighbor Intensity Difference (NID25_Busyness feature). Multivariate model showed GLCM25_Correlation (P = 0.007) and NID25_Busyness (P = 0.03) as 2 independent predictive variables for LR. The odds ratio values of GLCM25_Correlation and NID25_Busyness were 0.07 (95%CI 0.01-0.49) and 8.10 (95%CI 1.20-54.40), respectively. The area under the curve for the multivariate logistic regressive model was 0.851 (95%CI 0.724-0.978). At a median follow-up of 30 months, median PFS was 7 months (95%CI 6-NA); median OS was 11 months (95%CI 10-22 months).

CONCLUSIONS

This analysis identified a radiomic signature that correlates with LR. To confirm these results, prospective studies could identify patient sub-groups with different rates of radiation dose-response to define a more personalized SBRT approach.

Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development

PURPOSE

Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC).

RESULTS

The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM.

CONCLUSION

Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.

Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis

OBJECTIVES

There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC.

METHODS

In this retrospective study, 215 patients (average age: 57 years (18-87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set.

RESULTS

Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively.

CONCLUSION

FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions.

KEY POINTS

• Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

INTRODUCTION

Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy.

MATERIALS AND METHODS

Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used.

RESULTS

This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer.

CONCLUSIONS

Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer)

BACKGROUND

To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC).

METHODS

Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed.

RESULTS

In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R2: 0.546 for the internal cohort and P=0.014, Nagelkerke R2: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763.

CONCLUSIONS

Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.

A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer

PURPOSE

Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner.

METHODS

In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon-Mann-Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness.

RESULTS

Three features were selected: maximum fractal dimension with IB = 0-50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0-50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively.

CONCLUSIONS

The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.

Endorectal Ultrasound and Magnetic Resonance Imaging for Rectal Cancer Staging: A Modern Multimodality Approach

Preoperative staging represents a crucial point for the management, type of surgery, and candidacy for neoadjuvant therapy in patient with rectal cancer. The most recent clinical guidelines in oncology recommend an accurate preoperative evaluation in order to address early and advanced tumors to different therapeutic options. In particular, potential pitfalls may occur in the assessment of T3 tumors, which represents the most common stage at diagnosis. The depth of tumor invasion is known to be an important prognostic factor in rectal carcinoma; as a consequence, the T3 imaging classification has a substantial importance for treatment strategy and patient survival. However, the differentiation between tumor invasion of perirectal fat and mesorectal desmoplastic reactions remains a main goal for radiologists. Magnetic resonance imaging (MRI) is actually considered as the best imaging modality for rectal cancer staging. Although the endorectal ultrasound (ERUS) is the preferred staging method for early tumors, it could also be useful in identifying perirectal fat invasion. Moreover, the addiction of diffusion weighted imaging (DWI) improves the diagnostic performance of MRI in rectal cancer staging by adding functional information about rectal tumor and adjacent mesorectal tissues. This study investigated the diagnostic performance of conventional MRI alone, in combination with the DWI technique and ERUS in order to assess the best diagnostic imaging combination for rectal cancer staging.

Trends and outcome of neoadjuvant treatment for rectal cancer: A retrospective analysis and critical assessment of a 10-year prospective national registry on behalf of the Spanish Rectal Cancer Project

INTRODUCTION

Preoperative treatment and adequate surgery increase local control in rectal cancer. However, modalities and indications for neoadjuvant treatment may be controversial. Aim of this study was to assess the trends of preoperative treatment and outcomes in patients with rectal cancer included in the Rectal Cancer Registry of the Spanish Associations of Surgeons.

METHOD

This is a STROBE-compliant retrospective analysis of a prospective database. All patients operated on with curative intention included in the Rectal Cancer Registry were included. Analyses were performed to compare the use of neoadjuvant/adjuvant treatment in three timeframes: I)2006-2009; II)2010-2013; III)2014-2017. Survival analyses were run for 3-year survival in timeframes I-II.

RESULTS

Out of 14,391 patients,8871 (61.6%) received neoadjuvant treatment. Long-course chemo/radiotherapy was the most used approach (79.9%), followed by short-course radiotherapy ± chemotherapy (7.6%). The use of neoadjuvant treatment for cancer of the upper third (15-11 cm) increased over time (31.5%vs 34.5%vs 38.6%,p = 0.0018). The complete regression rate slightly increased over time (15.6% vs 16% vs 18.5%; p = 0.0093); the proportion of patients with involved circumferential resection margins (CRM) went down from 8.2% to 7.3%and 5.5% (p = 0.0004). Neoadjuvant treatment significantly decreased positive CRM in lower third tumors (OR 0.71, 0.59-0.87, Cochrane-Mantel-Haenszel P = 0.0008). Most ypN0 patients also received adjuvant therapy. In MR-defined stage III patients, preoperative treatment was associated with significantly longer local-recurrence-free survival (p < 0.0001), and cancer-specific survival (p < 0.0001). The survival benefit was smaller in upper third cancers.

CONCLUSION

There was an increasing trend and a potential overuse of neoadjuvant treatment in cancer of the upper rectum. Most ypN0 patients received postoperative treatment. Involvement of CRM in lower third tumors was reduced after neoadjuvant treatment. Stage III and MRcN + benefited the most.

Magnetic resonance severity index assessed by T1-weighted imaging for acute pancreatitis: correlation with clinical outcomes and grading of the revised Atlanta classification-a narrative review

Acute pancreatitis (AP) is a common disease that may involve pancreas and peripancreatic tissues with a prevalence of up to 50 per 100,000 individuals for year. The Atlanta classification was assessed for the first time in 1992 and modified in 2012 in order to describe morphological features of AP and its complications. AP can be morphologically distinguished in two main types: interstitial edematous pancreatitis (IEP) and necrotizing pancreatitis (NEP). This classification is very important because the presence of necrosis is directly linked to local or systemic complications, hospital stays and death. Magnetic resonance (MR) is very useful to characterize morphological features in AP and its abdominal complications. Particularly we would like to underline the diagnostic, staging and prognostic role of T1-weighted images with fat suppression that could be significant to assess many features of the AP inflammatory process and its complications (detection of the pancreatic contour, pancreatic necrosis, presence of haemorrhage). Signs of inflammatory and edema are instead observed by T1-weighted images. MR cholangiopancreatography (MRCP) is necessary to study the main pancreatic duct and the extrahepatic biliary tract and contrast-enhancement magnetic resonance imaging (MRI) allows to assess the extent of necrosis and vascular injuries.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on ¹⁸F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.

The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions

Background

The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions.

Methods

In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm²), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves.

Results

There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29.

Conclusions

A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.

MR Imaging Texture Analysis in the Abdomen and Pelvis

Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

OBJECTIVES

This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC).

METHODS

Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival.

RESULTS

In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046).

CONCLUSIONS

Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence.

KEY POINTS

• Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.

Delta-radiomics increases multicentre reproducibility: a phantom study

Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.

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.

Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software^©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.

Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features

Objectives

To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features.

Methods

A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann–Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features.

Results

Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image.

Conclusion

Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.