Artificial Intelligence in Radiation Therapy

Knowledge-Based RapidPlan Volumetric Modulated Arc Therapy Model in Nasopharyngeal Carcinoma

Purpose

RapidPlanTM (RP) Eclipse@, a commercial knowledge-based planning software, predicts radiation doses, enhancing treatment planning efficiency and quality. This study developed a nasopharyngeal carcinoma (NPC) volumetric modulated arc therapy RP model and assessed its quality and efficiency against manual plans.

Methods and Materials

The existing plans for 160 patients with NPC constituted the RP model training cohort. An additional 33 patients formed a testing cohort to compare RP and manual plans based on dose-volume histograms, isodose curves, physician plan scores, and selection.

Results

The RP plan could be completed within 1 hour. RP plans demonstrated superior conformity compared with manual plans in planning target volume 70. RP plans outperformed manual plans in reducing organs-at-risks (OARs) doses. For advanced T3/4 tumors, chiasma and optic nerve doses remained similar to manual plans. RP plans had higher physician-rated scores in dose-volume histograms of targets, OARs, isodose curves, and holistic scores. Clinical plan acceptance rates of RP plans reached 100%. Physicians chose RP plans over manual plans for 31, 30, and 28 patients for doctors A, B, and C, mainly because of superior OAR sparing and higher conformity.

Conclusions

The RP plan efficiently generates high-quality NPC volumetric modulated arc therapy plans. Further applications of RP Eclipse in single-institutional clinical flows and multi-institutional collaborations or clinical trials are warranted.

Multicentre prospective risk analysis of a fully automated radiotherapy workflow

Background and Purpose

Fully automated workflows (FAWs) for radiotherapy treatment preparation are feasible, but remain underutilized in clinical settings. A multicentre prospective risk analysis was conducted to support centres in managing FAW-related risks and to identify workflow steps needing improvement.

Material and Methods

Eight European radiotherapy centres performed a failure mode and effect analysis (FMEA) on a hypothetical FAW, with a manual review step at the end. Centres assessed occurrence, severity and detectability of provided, or newly added, failure modes to obtain a risk score. Quantitative analysis was performed on curated data, while qualitative analysis summarized free text comments.

Results

Manual review and auto-segmentation were identified as the highest-risk steps and the highest scoring failure modes were associated with inadequate manual review (high detectability and severity score), incorrect (i.e. outside of intended use) application of the FAW (high severity score) and protocol violations during patient preparation (high occurrence score). The qualitative analysis highlighted amongst others the risk of deviation from protocol and the difficulty for manual review to recognize automation errors. The risk associated with the technical parts of the workflow was considered low.

Conclusions

The FMEA analysis highlighted that points where people interact with the FAW were considered higher risk than lack of trust in the FAW itself. Major concerns were the ability of people to correctly judge output in case of low generalizability and increasing skill degradation. Consequently, educational programs and interpretative tools are essential prerequisites for widespread clinical application of FAWs.

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients

Objective.One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, with potential dosimetric degradation and small segment openings that increase delivery time. This work aims to replace this process with a fast deep learning-based method to provide deliverable MLC sequences almost instantaneously, potentially accelerating and enhancing online adaption.Approach.Daily MRIs and plans from 242 daily fractions of 49 abdomen cancer patients on a 1.5 T MR-Linac were used. The architecture included: (1) a recurrent conditional generative adversarial network model to predict segment shapes from a fluence map (FM), recurrently predicting each segment's shape; and (2) a linear matrix equation module to optimize the monitor units (MUs) weights of segments. Multiple models with different segment numbers per beam (4-7) were trained. The final MLC sequences with the smallest relative absolute errors were selected. The predicted MLC sequence was imported into treatment planning system for dose calculation and compared with the original plans.Main results.The gamma passing rate for all fractions was 99.7 ± 0.2% (2%/2 mm criteria) and 92.7 ± 1.6% (1%/1 mm criteria). The average number of segments per beam in the proposed method was 6.0 ± 0.6 compared to 7.5 ± 0.3 in the original plan. The average total MUs were reduced from 1641 ± 262 to 1569.5 ± 236.7 in the predicted plans. The estimated delivery time was reduced from 9.7 min to 8.3 min, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 s using a GTX1660TIGPU.Significance.The developed models can quickly and accurately generate an optimized, deliverable leaf sequence from a FM with fewer segments. This can seamlessly integrate into the current online replanning workflow, greatly expediting the daily plan adaptation process.

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions

This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving radiation therapy. As the field of radiation therapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiation therapy, image guided radiation therapy, and artificial intelligence promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect, and the combination of radiation therapy and immunotherapy create new avenues for innovation in clinical trials. The paper aims to provide an overview of these emerging technologies and discuss their challenges and opportunities in shaping the future of radiation oncology clinical trials. By synthesizing knowledge from experts across various disciplines, this white paper aims to present a foundation for the successful integration of these innovations into radiation therapy research and practice, ultimately enhancing patient outcomes and revolutionizing cancer care.

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications in neuro-oncology, including guiding clinical decisions and improving patient management. However, the lack of clarity on how AI arrives at predictions has hindered its clinical translation. Explainable AI (XAI) methods aim to improve trustworthiness and informativeness, but their success depends on considering end-users' (clinicians') specific context and preferences. User-Centered Design (UCD) prioritizes user needs in an iterative design process, involving users throughout, providing an opportunity to design XAI systems tailored to clinical neuro-oncology. This review focuses on the intersection of MR imaging interpretation for neuro-oncology patient management, explainable AI for clinical decision support, and user-centered design. We provide a resource that organizes the necessary concepts, including design and evaluation, clinical translation, user experience and efficiency enhancement, and AI for improved clinical outcomes in neuro-oncology patient management. We discuss the importance of multi-disciplinary skills and user-centered design in creating successful neuro-oncology AI systems. We also discuss how explainable AI tools, embedded in a human-centered decision-making process and different from fully automated solutions, can potentially enhance clinician performance. Following UCD principles to build trust, minimize errors and bias, and create adaptable software has the promise of meeting the needs and expectations of healthcare professionals.

A student trained convolutional neural network competing with a commercial AI software and experts in organ at risk segmentation

This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostate cancer (PCa) patients, specifically the bladder and rectum. The objective of this project was to develop a clinically applicable and robust artificial intelligence (AI) system to assist radiation oncologists in OAR segmentation. The CNN was trained using manual contours in CT-datasets from diagnostic 68Ga-PSMA-PET/CTs by a student, then validated (n = 30, PET/CTs) and tested (n = 16, planning CTs). Further segmentations were generated by a commercial artificial intelligence (cAI) software. The ground truth were manual contours from expert radiation oncologists. The performance was evaluated using the Dice-Sørensen Coefficient (DSC), visual analysis and a Turing test. The CNN yielded excellent results in both cohorts and OARs with a DSCmedian > 0.87, the cAI resulted in a DSC > 0.78. In the visual assessment, 67% (bladder) and 75% (rectum) of the segmentations were rated as acceptable for treatment planning. With a misclassification rate of 45.5% (bladder) and 51.1% (rectum), the CNN passed the Turing test. The metrics, visual assessment and the Turing test confirmed the clinical applicability and therefore the support in clinical routine.

A Review of Artificial Intelligence in Brachytherapy

Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography

BACKGROUND

Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images.

METHODS

To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images.

RESULTS

To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network.

CONCLUSION

Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Large-scale experiment in STEM education for high school students using artificial intelligence kit based on computer vision and Python

This study proposes an artificial intelligence (AI) kit for high school students in science, technology, engineering, and mathematics (STEM). The AI kit includes an edge AI machine and electronic components. A compact, purpose-built kit resembling a laptop was designed for ease of replication and portability. Utilizing pre-trained convolutional neural network models and computer vision algorithms, five Thai schools participated in on-site instructions. A quasi-experimental study assessed the students' learning outcomes using a paired sample t-test. Results revealed improved knowledge and reduced score variation. Additionally, gender analysis confirmed that both male and female students met the learning criteria. The students expressed satisfaction with the distinctive hardware and learning method employed during the class activities. Notably, the test results demonstrated that the AI kit enhanced students' enthusiasm and facilitated comprehension.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.