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

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