IMPROVING RADIOTHERAPY WORKFLOW: EVALUATION AND IMPLEMENTATION OF DEEP LEARNING AUTO-SEGMENTATION IN A MULTI-USER ENVIRONMENT, AND DEVELOPMENT OF AUTOMATIC CONTOUR QUALITY ASSURANCE SYSTEM

Radiotherapy is a frequently used therapeutic modality for cancer patients. Accurately contouring of tumors and organs at risk (OARs) is critical for developing optimal treatment plans in radiotherapy, especially after the implementation of Intensity-modulated radiation therapy (IMRT) and Stereotactic Body Radiation Therapy (SBRT). The manual contouring process is time-consuming and suffers from inter-observer variations. However, manual contouring is often hindered by laborious clinical duties, leading to reduced effectiveness, and increased segmentation errors due to fatigue. Additionally, online adaptive radiation therapy(ART), which has been shown to benefit patient outcomes, places higher demands on contouring and quality assurance (QA) speed. Recently, deep learning auto-segmentation (DLAS) has emerged as an accurate tool for contouring in many anatomical sites. However, DLAS\u27s black-box nature has limited its widespread clinical implementation. Robust evaluations are required prior to the clinical implementation. In this thesis, we present our comprehensive validation approach for assessing the clinical acceptability of DLAS contours in the male pelvis region for automated prostate treatment planning. We then evaluated the DLAS model\u27s capacity for continuous improvement and generalizability and successfully adopted it in a multi-user environment. Additionally, we provided an implementation workflow for this software that can be used by other clinical users. Manual reviewing contour is a time-consuming process that is prone to errors and omissions, leading to dosimetric uncertainties and lower quality of radiation treatment. To assist with the manual contour review process, an automated contouring QA tool is necessary. We proposed a machine learning-based methodology for an automated contour quality assurance system that detects errors in manual contouring, using the precise DLAS contour as a reference. Moreover, we established a knowledge-based contour QA system that can localize and categorize contour errors for improved accuracy and efficiency. Overall, this dissertation provides a more comprehensive understanding of DLAS in a clinical multi-user environment, which will improve the quality and safety of the radiotherapy workflow

Quo vadis radiotherapy? Technological advances and the rising problems in cancer management

Extent: 10p.Purpose. Despite the latest technological advances in radiotherapy, cancer control is still challenging for several tumour sites. The survival rates for the most deadly cancers, such as ovarian and pancreatic, have not changed over the last decades. The solution to the problem lies in the change of focus: from local treatment to systemic therapy. The aim of this paper is to present the current status as well as the gaps in radiotherapy and, at the same time, to look into potential solutions to improve cancer control and survival. Methods. The currently available advanced radiotherapy treatment techniques have been analysed and their cost-effectiveness discussed. The problem of systemic disease management was specifically targeted. Results. Clinical studies show limited benefit in cancer control from hadron therapy. However, targeted therapies together with molecular imaging could improve treatment outcome for several tumour sites while controlling the systemic disease. Conclusion. The advances in photon therapy continue to be competitive with the much more expensive hadron therapy. To justify the cost effectiveness of proton/heavy ion therapy, there is a need for phase III randomised clinical trials. Furthermore, the success of systemic disease management lies in the fusion between radiation oncology technology and microbiology.Barry J. Allen, Eva Bezak, and Loredana G. Marc

p16 expression in cutaneous squamous cell carcinoma of the head and neck is not associated with integration of high risk HPV DNA or prognosis

Head and neck cutaneous squamous cell carcinoma (HNcSCC) can present with cervical metastases without an obvious primary. Immunohistochemistry for p16 is established as a surrogate marker of human papillomavirus (HPV) in oropharyngeal cancer. p16 expression in HNcSCC needs to be elucidated to determine its utility in predicting the primary site. The aim of this study was to evaluate the rate of p16 expression in HNcSCC and its association with prognostic factors and survival. p16 immunohistochemistry was performed on 166 patients with high risk HNcSCC (2000-2013) following histopathology review. Chromogenic in situ hybridisation (CISH) for HPV was performed. Fifty-three (31.9%) cases showed strong, diffuse nuclear and cytoplasmic p16 expression including 14 (41%) non-metastatic and 39 (29.5%) metastatic tumours (p = 0.21). HPV CISH was negative in all cases. p16 expression significantly increased with poorer differentiation (p = 0.033), but was not associated with size (p = 0.30), depth of invasion (p = 0.94), lymphovascular invasion (p = 0.31), perineural invasion (p = 0.69), keratinisation (p = 0.99), number of involved nodes (p = 0.64), extranodal extension (p = 0.59) or survival. Nearly 32% of HNcSCCs, particularly poorly differentiated HNcSCCs, show p16 expression. A primary HNcSCC should be considered in p16 positive neck node metastases in regions with high prevalence of HNcSCC. p16 expression is not associated with improved survival in HNcSCC

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)