Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

While cross-sectional imaging has seen continuous progress and plays an undiscussedpivotal role in the diagnostic management and treatment planning of patients with rectal cancer, alargely unmet need remains for improved staging accuracy, assessment of treatment response andprediction of individual patient outcome. Moreover, the increasing availability of target therapies hascalled for developing reliable diagnostic tools for identifying potential responders and optimizingoverall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fullyevolving research topic, which could harness the power of modern computer technology to generatequantitative information from imaging datasets based on advanced data-driven biomathematicalmodels, potentially providing an added value to conventional imaging for improved patient manage-ment. The present study aimed to illustrate the contribution that current radiomics methods appliedto magnetic resonance imaging can offer to managing patients with rectal cancer

Multiparametric image modelling:predicting treatment response in rectal cancer

Patients with advanced rectal cancer are typically treated with chemo- and radiation therapy (chemoradiation) followed by a major surgery where the entire rectum is removed. Some patients respond so well to chemoradiation that here is hardly any tumor present after treatment. This is why major surgery is increasingly omitted in patients that respond well to chemoradiation and instead these patients are treated “organ-saving” – i.e. with only a minor intervention or even without surgery. In this thesis, the possibility to predict treatment outcome to chemoradiation beforehand based on the available clinical data and imaging data has been studied using prediction models. If it is known upfront whether a patient will respond well to chemoradiation, this may offer possibilities to further personalize treatment to a specific patient. The predictive values of various imaging techniques were compared. In addition, the influence of data variations on reproducibility was investigated. The results of these studies offer important insights that are valuable for the development of future prediction models

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making

Radiomics analyses for outcome prediction in patients with locally advanced rectal cancer and glioblastoma multiforme using multimodal imaging data

Personalized treatment strategies for oncological patient management can improve outcomes of patient populations with heterogeneous treatment response. The implementation of such a concept requires the identification of biomarkers that can precisely predict treatment outcome. In the context of this thesis, we develop and validate biomarkers from multimodal imaging data for the outcome prediction after treatment in patients with locally advanced rectal cancer (LARC) and in patients with newly diagnosed glioblastoma multiforme (GBM), using conventional feature-based radiomics and deep-learning (DL) based radiomics. For LARC patients, we identify promising radiomics signatures combining computed tomography (CT) and T2-weighted (T2-w) magnetic resonance imaging (MRI) with clinical parameters to predict tumour response to neoadjuvant chemoradiotherapy (nCRT). Further, the analyses of externally available radiomics models for LARC reveal a lack of reproducibility and the need for standardization of the radiomics process. For patients with GBM, we use postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w MRI for the detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS). We show that DL models built on MET-PET have an improved diagnostic and prognostic value as compared to MRI

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer

Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T-2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice

Normalization strategies in multi-center radiomics abdominal MRI: systematic review and meta-analyses

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside

Prediction of Pre-Operative Local Staging and Optimising Treatment Response to Neoadjuvant Therapy in Colorectal Cancer.

The presence of abnormal Lymph Nodes (LNs) in patients with colorectal cancer is an essential determinant of prognosis and guides treatment options (surgical and medical). Staging with Computed Tomography (CT) is somewhat inaccurate in determining true nodal status. As a result, either approximate estimates must be made on imaging, or definitive nodal staging determined by surgical resection before recommendations about the risk vs benefit of chemotherapy can be made reliably. Patients with advanced rectal cancer are commonly referred for neoadjuvant therapy as part of standard care treatment protocols based on Magnetic Resonance Imaging (MRI) local staging. Following neoadjuvant therapy, many patients then undergo surgical resection. However, a significant proportion achieve a complete Clinical Response (cCR) with modern neoadjuvant treatment, and these patients are increasingly offered non-operative management and surveillance with the goal of organ preservation. Accurate clinical staging parameters and predictive markers of tumour response may help guide more personalised treatment strategies and identify potential candidates for non-operative management more accurately. Within the past decade, a promising new strategy termed Total Neoadjuvant Therapy (TNT) has been shown to improve compliance with chemotherapy, by delivering this sequentially with chemoradiotherapy prior to surgery in patients with rectal cancer. TNT has the potential to reduce distant failure risk and provide significantly higher rates of pathological Complete Response (pCR) and cCR with an opportunity to manage patients non-operatively, however, optimal treatment sequencing of radiotherapy and chemotherapy remains somewhat unclear. Pre-operative prediction of nodal status in colon cancer, neoadjuvant treatment response in rectal cancer, as well as optimal sequencing of neoadjuvant therapy, represent major areas of weakness in current treatment paradigms in colorectal surgical oncology. Furthermore, they are all areas of active research, and frequently tie in together during Multi-Disciplinary Team meeting (MDT) discussions in clinical practice. The aims of this thesis are: Firstly, to investigate Artificial Intelligence (AI) models for prediction of LN status on preoperative staging CT in patients with colon cancer. Secondly, to identify pre-treatment factors predictive of Complete Response (CR) following neoadjuvant therapy in patients with Locally Advanced Rectal Cancer (LARC), specifically sarcopenia, clinical and biochemical factors. Lastly, to determine whether a Personalised Total Neoadjuvant Therapy (pTNT) protocol with sequencing tailored to the clinical stage at presentation results in better short-term oncological outcomes compared to a uniform protocol for all patients with advanced rectal cancer. To achieve these aims, two meta-analyses were performed to identify the gaps in the field of AI LN detection. The first, focused on the accuracy of deep learning algorithms and radiomics models compared with radiologist assessment in the diagnosis of lymphadenopathy in patients with abdominopelvic malignancies and the second solely focused on colorectal cancer. Subsequently, a deep learning model was developed to assess LN status on staging CT in patients with colon cancer, and the model’s performance was compared with baseline results of a prospective study evaluating the accuracy of preoperative staging. A systemic review and meta-analysis were performed to identify and assess AI segmentation models able to predict sarcopenia using CT scans. Following this, an institutional colorectal cancer database was interrogated to determine if sarcopenia or clinical and biochemical markers were associated with tumour response in patients with LARC. Prospective data was collected on patients in two hospitals who underwent pTNT based on their clinical staging at presentation for the treatment of advanced rectal cancer. A cohort study was performed to summarise tumour response, chemotherapy compliance and the toxicity profile of patients. An additional multicentre retrospective cohort analysis comparing pTNT over a 3-year period to a historical cohort of randomised control trial patients who had extended chemotherapy in the wait period (xCRT) or standard long course Chemoradiotherapy (sCRT) was conducted. The two meta-analyses determined that deep learning assessment of LNs demonstrated the greatest potential for assessment of LN without the need for surgery, with MRI for rectal cancer and CT in colon cancer providing the greatest accuracy. Our clinical studies demonstrated that radiological assessment remains the most effective preoperative method of staging LNs, with histology considered the gold standard. Deep learning assessment using a ResNet-50 framework is limited to very low accuracy and specificity in detecting abnormal LNs when compared to the radiologist’s assessment. It is likely that the poor performance of the deep learning model is attributed to the lack of features extracted from the CT scans. The meta-analysis found that deep learning segmentation models can accurately predict sarcopenia using CT scans. However, sarcopenia was not found to be a predictor of pCR in patients with LARC. The clinical predictors of good tumour response after neoadjuvant therapy for rectal cancer were found to be a clinical T2 stage and Body Mass Index (BMI) ≥25kg/m2. Pre-treatment biochemical markers were not predictive of tumour response after neoadjuvant therapy for rectal cancer. Our research found that over 40% of the patients who underwent pTNT for the treatment of advanced rectal cancer demonstrated a complete response in the primary tumour (pCR and/or cCR) resulting in a high rate of organ preservation. Furthermore, 45% of the patients with stage M1 disease achieved a complete M1 response. Compliance with chemotherapy was over 95% and toxicity was lower than expected. When comparing a pTNT approach with xCRT or sCRT in patients with LARC, there was a significant difference in complete response and cCR rate favouring the pTNT group compared to the xCRT and sCRT groups. In conclusion, these results suggest that a deep learning model with a ResNet-50 framework does not serve as a reliable staging tool for the prediction of LN status using preoperative staging CT in patients with colon cancer. Despite a large volume of research, the ability to predict which patients are likely to achieve a complete response by measuring pre-treatment sarcopenia, clinical and biochemical markers remains elusive. Early results of a pTNT approach tailoring sequencing of neoadjuvant chemotherapy to disease risk at presentation are encouraging and compare favourably to xCRT and sCRT in patients with advanced rectal cancer.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 202