Literature review for a business plan on automated contouring

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Automated contouring in radiation oncology represents a transformative application of artificial intelligence (AI) and machine learning technologies to address one of the most time-consuming and critical aspects of radiation therapy planning. Contouring—the process of precisely delineating tumor targets and surrounding healthy tissues (organs at risk)—traditionally requires radiation oncologists to manually outline these structures on CT, MRI, or PET scans. This process typically consumes 2-4 hours per patient case and introduces significant variability between clinicians.

Automated contouring leverages deep learning algorithms, particularly convolutional neural networks, to automatically identify and delineate anatomical structures and tumor volumes with increasing accuracy. These AI-powered solutions can reduce contouring time by up to 90% while maintaining or even improving delineation accuracy. Modern systems can identify dozens of anatomical structures across various body sites including head and neck, thorax, abdomen, and pelvis.

The technology represents a paradigm shift in radiation oncology workflow, moving from a fully manual process to a computer-assisted or fully automated approach with human supervision. By standardizing contours, automated systems reduce inter-observer variability and help ensure treatment plans more consistently follow clinical guidelines and best practices. This advancement allows radiation oncology departments to increase throughput, standardize care, and potentially improve treatment outcomes while reducing clinician workload.

Market need and Problem Statement

The radiation oncology field faces a critical challenge in the contouring process, which represents a significant bottleneck in treatment planning workflow. Manual contouring—the traditional approach to delineating tumors and organs at risk—is exceptionally time-consuming, with studies showing it typically requires 2-4 hours per patient case. This labor-intensive process not only strains limited clinical resources but also delays treatment initiation, which can directly impact patient survival rates (Anand et al., 2021). Using an observational cohort study of 25 216 patients from the National Cancer Database, a survival benefit to a shorter time from surgery to the start of radiation (TS-RT) for patients with head and neck squamous cell carcinoma showed that a TS-RT of 42 days or less was associated with improved survival compared with 50 days or longer; a delay of 1 week resulted in inferior outcomes for patients with tonsil tumors.(Harris et al., 2018).

A fundamental problem with manual contouring is the high degree of variability in results. Contouring outcomes vary significantly between clinicians (inter-observer variability) and even when performed by the same clinician at different times (intra-observer variability) (Chebrolu et al., 2014)(Heilemann et al., 2023). This variability stems from differences in radiation oncology training, experience levels, and interpretation of imaging studies (Yakar et al., 2021). Such inconsistencies can significantly impact dose/volume-based plan evaluation, clinical outcomes, and introduce bias in clinical trials (Heilemann et al., 2023)(Boero et al., 2016).

The market need for automated contouring solutions is further amplified by the predicted substantial shortage of radiation oncologists in the United States, United Kingdom, and low/middle-income countries (Anand et al., 2021). This workforce shortage creates an urgent need for technologies that can improve efficiency without compromising quality. Research indicates that computer-assisted contouring methods can provide significant time savings—26-29% for experienced physicians and 38-47% for less experienced physicians (Ikushima et al., 2017)(Chao et al., 2007).

Beyond operational efficiency, the clinical impact of contouring quality is profound. The uncertainties in gross tumor volume (GTV) regions significantly impact the precision of entire radiation treatment courses (Ikushima et al., 2017). This is particularly critical in advanced techniques like stereotactic body radiation therapy (SBRT), where precise targeting is essential for delivering higher doses to tumors while sparing surrounding normal tissue. Auto-segmentation addresses these challenges by providing faster, more consistent results that are less dependent on user experience (Doolan et al., 2023)(Sharp et al., 2014).

A significant challenge in developing effective auto-segmentation solutions is the relative scarcity of curated multi-expert observer datasets sufficiently large to train machine learning models, particularly for complex anatomical areas like the head and neck that demonstrate high interobserver segmentation variability (Lin et al., 2022)(Mak et al., 2019). Despite these challenges, advancing auto-segmentation technologies offers tremendous potential for standardization across institutions and users, enabling improvements in both routine clinical practice and adaptive radiotherapy approaches (Segedin et al., 2016)(Heilemann et al., 2023).