Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure

Delta radiomics: an updated systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

Longitudinal Image Data for Outcome Modeling

In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

Rectal Cancer MRI Guided Radiotherapy: A Practical Review for the Physician

MR-guided radiotherapy is a treatment approach that combines the advantages of magnetic resonance imaging (MRI) with the precision of radiation therapy. This practical review provides an overview of the current state-of-the-art of MR-guided radiotherapy for rectal cancer, including its technical aspects, clinical outcomes, and existing limitations. Even though some studies have demonstrated the feasibility and safety of this treatment modality, challenges remain in terms of patient selection, treatment planning optimization, and long-term follow-up. Despite these issues, MR-guided radiotherapy shows promise as a potentially valuable rectal cancer treatment approach.

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.

Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review

Introduction

Gastric cancer (GC) is an aggressive and clinically heterogeneous tumor, and better risk stratification of lymph node metastasis (LNM) could lead to personalized treatments. The role of radiomics in the prediction of nodal involvement in GC has not yet been systematically assessed. This study aims to assess the role of radiomics in the prediction of LNM in GC.

Methods

A PubMed/MEDLINE systematic review was conducted to assess the role of radiomics in LNM. The inclusion criteria were as follows: i. original articles, ii. articles on radiomics, and iii. articles on LNM prediction in GC. All articles were selected and analyzed by a multidisciplinary board of two radiation oncologists and one surgeon, under the supervision of one radiation oncologist, one surgeon, and one medical oncologist.

Results

A total of 171 studies were obtained using the search strategy mentioned on PubMed. After the complete selection process, a total of 20 papers were considered eligible for the analysis of the results. Radiomics methods were applied in GC to assess the LNM risk. The number of patients, imaging modalities, type of predictive models, number of radiomics features, TRIPOD classification, and performances of the models were reported.

Conclusions

Radiomics seems to be a promising approach for evaluating the risk of LNM in GC. Further and larger studies are required to evaluate the clinical impact of the inclusion of radiomics in a comprehensive decision support system (DSS) for GC.

KIT 1 (Keep in Touch) Project-Televisits for Cancer Patients during Italian Lockdown for COVID-19 Pandemic: The Real-World Experience of Establishing a Telemedicine System

To evaluate the adoption of an integrated eHealth platform for televisit/monitoring/consultation during the COVID-19 pandemic.

METHODS

During the lockdown imposed by the Italian government during the COVID19 pandemic spread, a dedicated multi-professional working group was set up in the Radiation Oncology Department with the primary aim of reducing patients' exposure to COVID-19 by adopting de-centralized/remote consultation methodologies. Each patient's clinical history was screened before the visit to assess if a traditional clinical visit would be recommended or if a remote evaluation was to be preferred. Real world data (RWD) in the form of patient-reported outcomes (PROMs) and patient reported experiences (PREMs) were collected from patients who underwent televisit/teleconsultation through the eHealth platform.

RESULTS

During the lockdown period (from 8 March to 4 May 2020) a total of 1956 visits were managed. A total of 983 (50.26%) of these visits were performed via email (to apply for and to upload of documents) and phone call management; 31 visits (1.58%) were performed using the eHealth system. Substantially, all patients found the eHealth platform useful and user-friendly, consistently indicating that this type of service would also be useful after the pandemic.

CONCLUSIONS

The rapid implementation of an eHealth system was feasible and well-accepted by the patients during the pandemic. However, we believe that further evidence is to be generated to further support large-scale adoption.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

pCR and 2-Year Disease-Free Survival: A Combination of the Two Endpoints as a New Classification for Locally Advanced Rectal Cancer Patients-An Updated Pooled Analysis of Eleven International Randomized Trials

LARC is managed by multimodal treatments whose intensity can be highly modulated. In this context, we need surrogate endpoints to help predict long-term outcomes and better personalize treatments. A previous study identified 2yDFS as a stronger predictor of OS than pCR in LARC patients undergoing neoadjuvant RT. The aim of this pooled analysis was to assess the role of pCR and 2yDFS as surrogate endpoints for OS in a larger cohort. The pooled and subgroup analyses were performed on large rectal cancer randomized trial cohorts who received long-course RT. Our analysis focused on the evaluation of OS in relation to the pCR and 2-year disease status. A total of 4600 patients were analyzed. Four groups were identified according to intermediate outcomes: 12% had both pCR and 2yDFS (the better); 67% achieved 2yDFS but not pCR (the good); 1% had pCR but not 2yDFS; and 20% had neither pCR nor 2yDFS (the bad). The pCR and 2yDFS were favorably associated with OS in the univariate analysis, and 2yDFS maintained a statistically significant association in the multivariate analysis independently of the pCR status. The combination of the pCR and 2yDFS results in a strong predictor of OS, whereas failure to achieve 2yDFS carries a poor prognosis regardless of the pCR status. This new stratification of LARC patients could help design predictive models where the combination of 2yDFS and pCR should be employed as the primary outcome.

Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer

BACKGROUND

The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT).

METHODS

Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS.

RESULTS

Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set.

CONCLUSIONS

The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objective

For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.

Methods

In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).

Results

For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).

Conclusions

This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

Integration of art and technology in personalized radiation oncology care: Experiences, evidence, and perspectives

Cancer diagnoses expose patients to traumatic stress, sudden changes in daily life, changes in the body and autonomy, with even long-term consequences, and in some cases, to come to terms with the end-of-life. Furthermore, rising survival rates underline that the need for interventions for emotional wellbeing is in growing demand by patients and survivors. Cancer patients frequently have compliance problems, difficulties during treatment, stress, or challenges in implementing healthy behaviors. This scenario was highlighted during the COVID-19 emergency. These issues often do not reach the clinical attention of dedicated professionals and could also become a source of stress or burnout for professionals. So, these consequences are evident on individual, interpersonal, and health system levels. Oncology services have increasingly sought to provide value-based health care, considering resources invested, with implications for service delivery and related financing mechanisms. Value-based health care can improve patient outcomes, often revealed by patient outcome measures while seeking balance with economical budgets. The paper aims to show the Gemelli Advanced Radiation Therapy (ART) experience of personalizing the patients' care pathway through interventions based on technologies and art, the personalized approach to cancer patients and their role as "co-stars" in treatment care. The paper describes the vision, experiences, and evidence that have guided clinical choices involving patients and professionals in a co-constructed therapeutic pathway. We will explore this approach by describing: the various initiatives already implemented and prospects, with particular attention to the economic sustainability of the paths proposed to patients; the several pathways of personalized care, both from the patient's and healthcare professional perspective, that put the person's experience at the Gemelli ART Center. The patient's satisfaction with the treatment and economic outcomes have been considered. The experiences and future perspectives described in the manuscript will focus on the value of people's experiences and patient satisfaction indicators, patients, staff, and the healthcare organization.

Art and digital technologies to support resilience during the oncological journey: The Art4ART project

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Digital technologies can be useful in welcoming patients using the beauty of art.

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Cancer patients typically need to be supported in their treatment pathway.

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Digital entertainment can become a resilience-enhancing strategy.

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Art4ART project offers an art-based digital supporting patients’ resilience.

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Art4ART offers a research platform about the role of humanities as cure.

Introduction

New digital technologies can become a tool for welcoming the patient through the artistic dimension. Cancer patients, in particular, need support that accompanies and supports them throughout their treatment.

Materials and methods

The Art4ART project consist in the structural proposal to cancer patients of a web-based digital platform containing several forms of art as video-entertainments; a multimedia immersive room; an art-based welcoming of the patients with several original paintings; an environment with a peacefulness vertical garden; a reconceptualization of the chemotherapy-infusion seats. Data regarding patients’ preference and choices will be stored and analysed also using artificial intelligence (AI) algorithm to measure and predict impact indicators regarding clinical outcomes (survival and toxicity), psychological indicators. Moreover, the same digital platform will contribute to a better organization of the activities.

Discussion

Through the systematic acquisition of patient preferences and through integration with other clinical parameters, it will be possible to measure the clinical, psychological, organisational, and social impact of the newly implemented Art4ART project. The use of digital technology leads us to apply the reversal of viewpoint from therapeutic acts to patient-centred care.

Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer

In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.

A primer on texture analysis in abdominal radiology

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially ¹²⁵I CT-guided radioactive seed implant brachytherapy.

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.

Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

Background

To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.

Methods

Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.

Results

Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).

Conclusions

Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02053-y.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Simple Summary

Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

Purpose

Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS).

Methods

Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon–Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC).

Results

48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92).

Conclusions

The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results.

THUNDER 2: THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI guided radiotherapy

Background

Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients.

The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model.

Methods

Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously.

A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11^th fraction to reach 60.1 Gy.

At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life.

The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome.

Discussion

This clinical trial investigates the impact of RT dose escalation in poor responder LARC patients identified using ERI, with the aim of increasing the probability of CR and consequently an organ preservation benefit in this group of patients.

Trial registration

ClinicalTrials.gov Identifier: NCT04815694 (25/03/2021).

The impact of radiomics in diagnosis and staging of pancreatic cancer

INTRODUCTION

Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered.

RESULTS

A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction.

DISCUSSION

Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.

Fractal-Based Radiomic Approach to Tailor the Chemotherapy Treatment in Rectal Cancer: A Generating Hypothesis Study

Introduction

The aim of this study was to create a radiomic model able to calculate the probability of 5-year disease-free survival (5yDFS) when oxaliplatin (OXA) is or not administered in patients with locally advanced rectal cancer (LARC) and treated with neoadjuvant chemoradiotherapy (nCRT), allowing physicians to choose the best chemotherapy (CT) regimen.

Methods

LARC patients with cT3–4 cN0 or cT1–4 cN1–2 were treated according to an nCRT protocol that included concomitant CT schedules with or without OXA and radiotherapy dose of 55 Gy in 25 fractions. Radiomic analysis was performed on the T2-weighted (T2-w) MR images acquired during the initial tumor staging. Statistical analysis was performed separately for the cohort of patients treated with and without OXA. The ability of every single radiomic feature in predicting 5yDFS as a univariate analysis was assessed using the Wilcoxon–Mann–Whitney (WMW) test or t-test. Two logistic models (one for each cohort) were calculated, and their performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).

Results

A total of 176 image features belonging to four families (morphological, statistical, textural, and fractal) were calculated for each patient. At the univariate analysis, the only feature showing significance in predicting 5yDFS was the maximum fractal dimension of the subpopulation identified considering 30% and 50% as threshold levels (maxFD_30–50). Once the models were developed using this feature, an AUC of 0.67 (0.57–0.77) and 0.75 (0.56–0.95) was obtained for patients treated with and without OXA, respectively. A maxFD_30–50 >1.6 was correlated to a higher 5yDFS probability in patients treated with OXA.

Conclusion

This study suggests that radiomic analysis of MR T2-w images can be used to define the optimal concomitant CT regimen for stage III LARC cancer patients. In particular, by providing an indication of the gross tumor volume (GTV) spatial heterogeneity at initial staging, maxFD_30–50 seems to be able to predict the probability of 5yDFS. New studies including a larger cohort of patients and external validation sets are recommended to verify the results of this hypothesis-generating study.

Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer

OBJECTIVE

To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC).

METHODS

We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation.

RESULTS

The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05).

CONCLUSION

The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.

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.

Delta radiomics: a systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.

Overview of serum and tissue markers in colorectal cancer: a primer for radiologists

Serum and tissue tumor markers provide crucial information in the diagnosis, treatment, and follow-up of colorectal cancers. Tissue tumor markers are increasingly used for determination of targeted chemotherapy planning based on genotyping of tumor cells. Recently, plasma-based technique of liquid biopsy is being evaluated for providing tumor biomarkers in the management of colorectal cancer. Tumor markers are commonly used in conjunction with imaging during initial staging, treatment determination, response assessment, and determination of recurrence or metastatic disease. Knowledge of tumor markers and their association with radiological findings is thus crucial for radiologists. Additionally, various novel imaging techniques are being evaluated as potential noninvasive imaging biomarkers to predict tumor genotypes, features, and tumor response. We review and discuss the potential role of these newer imaging techniques.

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.

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.

Large Bowel Ischemia/Infarction: How to Recognize It and Make Differential Diagnosis? A Review

Ischemic colitis represents the most frequent form of intestinal ischemia occurring when there is an acute impairment or chronic reduction in the colonic blood supply, resulting in mucosal ulceration, inflammation, hemorrhage and ischemic necrosis of variable severity. The clinical presentation is variable and nonspecific, so it is often misdiagnosed. The most common etiology is hypoperfusion, almost always associated with generalized atherosclerotic disease. The severity ranges from localized and transient ischemia to transmural necrosis of the bowel wall, becoming a surgical emergency, with significant associated morbidity and mortality. The diagnosis is based on clinical, laboratory suspicion and radiological, endoscopic and histopathological findings. Among the radiological tests, enhanced-CT is the diagnostic investigation of choice. It allows us to make the diagnosis in an appropriate clinical setting, and to define the entity of the ischemia. MR may be adopted in the follow-up in patients with iodine allergy or renal dysfunctions, or younger patients who should avoid radiological exposure. In the majority of cases, supportive therapy is the only required treatment. In this article we review the pathophysiology and the imaging findings of ischemic colitis.