Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care

PURPOSE

Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world.

METHODS

The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale.

RESULTS

For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%).

CONCLUSION

The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer fully automated radiotherapy treatment planning tools to clinics with limited resources. The goal is to help clinical teams scale their efforts, thus reaching more patients with cancer. The user connects to the RPA via a webpage, completes a Service Request (prescription and information about the radiotherapy targets), and uploads the patient's CT image set. The RPA offers two approaches to automated planning. In one-step planning, the system uses the Service Request and CT scan to automatically generate the necessary contours and treatment plan. In two-step planning, the user reviews and edits the automatically generated contours before the RPA continues to generate a volume-modulated arc therapy plan. The final plan is downloaded from the RPA website and imported into the user's local treatment planning system, where the dose is recalculated for the locally commissioned linac; if necessary, the plan is edited prior to approval for clinical use.

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant

PURPOSE

Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access).

DESIGN

In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology.

RESULTS

RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain.

CONCLUSION

The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.

Science diplomacy in medical physics - an international perspective

Purpose

Science diplomacy in medical physics is a relatively young research field and translational practice that focuses on establishing international collaborations to address some of the questions biomedical professionals face globally. This paper aims to present an overview of science diplomacy in medical physics, from an international perspective, illustrating the ways collaborations within and across continents can lead to scientific and professional achievements that advance scientific growth and improve patients care.

Methods

Science diplomacy actions were sought that promote collaborations in medical physics across the continents, related to professional and scientific aspects alike.

Results

Several science diplomacy actions have been identified to promote education and training, to facilitate research and development, to effectively communicate science to the public, to enable equitable access of patients to healthcare and to focus on gender equity within the profession as well as healthcare provision. Scientific and professional organizations in the field of medical physics across all continents have adopted a number of efforts in their aims, many of them with great success, to promote science diplomacy and to foster international collaborations.

Conclusions

Professionals in medical physics can advance through international cooperation, by building strong communication across scientific communities, addressing rising demands, exchange scientific information and knowledge.

Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience

BACKGROUND

The high-dose rate (HDR) brachytherapy treatment planning workflow for cervical cancer is a labor-intensive, time-consuming, and expertise-driven process. These issues are amplified in low/middle-income countries with large deficits in experienced healthcare professionals. Automation has the ability to substantially reduce bottlenecks in the planning process but often require a high level of expertise to develop.

PURPOSE

To implement the out of the box self-configuring nnU-Net package for the auto-segmentation of the organs at risk (OARs) and high-risk CTV (HR CTV) for Ring-Tandem (R-T) HDR cervical brachytherapy treatment planning.

METHODS

The computed tomography (CT) scans of 100 previously treated patients were used to train and test three different nnU-Net configurations (2D, 3DFR, and 3DCasc). The performance of the models was evaluated by calculating the Sørensen-dice similarity coefficient, Hausdorff distance (HD), 95^th percentile Hausdorff distance, mean surface distance (MSD), and precision score for 20 test patients. The dosimetric accuracy between the manual and predicted contours was assessed by looking at the various dose volume histogram (DVH) parameters and volume differences. Three different radiation oncologists (ROs) scored the predicted bladder, rectum, and HR CTV contours generated by the best performing model. The manual contouring, prediction, and editing times were recorded.

RESULTS

The mean DSC, HD, HD95, MSD and precision scores for our best performing model (3DFR) were 0.92/7.5 mm/3.0 mm/ 0.8 mm/0.91 for the bladder, 0.84/13.8 mm/5.3 mm/1.4 mm/0.84 for the rectum, and 0.81/8.5 mm/6.0 mm/2.2 mm/0.80 for the HR CTV. Mean dose differences (D_2cc/90% ) and volume differences were 0.08 Gy/1.3 cm³ for the bladder, 0.02 Gy/0.7 cm³ for the rectum, and 0.33 Gy/1.5 cm³ for the HR CTV. On average, 65% of the generated contours were clinically acceptable, 33% requiring minor edits, 2% required major edits, and no contours were rejected. Average manual contouring time was 14.0 min, while the average prediction and editing times were 1.6 and 2.1 min, respectively.

CONCLUSION

Our best performing model (3DFR) provided fast accurate auto generated OARs and HR CTV contours with a large clinical acceptance rate.

Understanding the challenges of delivering radiotherapy in low- and middle-income countries in Africa

BACKGROUND

Access to high quality radiotherapy (RT) continues to be a major issue across Africa with Africa having just 34% of its optimal capacity.

METHODS

We co-developed a survey with clinical, academic and policy stakeholders designed to provide a structured assessment of the barriers and enablers to RT capacity building in Africa. The survey covered nine key themes including funding, procurement, education and training. The survey was sent to RT professionals in 28 countries and the responses underwent qualitative and quantitative assessment.

RESULTS

We received completed questionnaires from 26 African countries. Funding was considered a major issue, specifically the lack of a ring fenced funds from the Ministry of Health for radiotherapy and the consistency of revenue streams which relates to a lack of prioritisation for RT. In addition to a significant shortfall in RT workforce disciplines, there is a general lack of formal education and training programmes. 13/26 countries reported having some IAEA support for RT for education and training. Solutions identified to improve access to RT include a) increasing public awareness of its essential role in cancer treatment; b) encouraging governments to simplify procurement and provide adequate funding for equipment; c) increasing training opportunities for all radiotherapy disciplines and d) incentivizing staff retention.

CONCLUSION

This survey provides unique information on challenges to delivering and expanding radiotherapy services in Africa. The reasons are heterogonous across countries but one key recommendation would be for national Cancer Control plans to directly consider radiotherapy and specifically issues of funding, equipment procurement, servicing and training.

POLICY SUMMARY

The study demonstrates the importance of mixed methods research to inform policy and overcome barriers to radiotherapy capacity and capability in LMICs.

Safety measures in selected radiotherapy centres within Africa in the face of Covid-19

Radiotherapy is life-saving treatment which ought to be guaranteed for all cancer patients who are indicated. While this is so, it is incumbent on the management of radiotherapy centres to ensure that patients, patient care-givers and radiotherapy personnel are at all times safe within the radiotherapy facility. Cancer patients are known to have increased risk for respiratory viruses like Covid-19 due to the compromised immune state of such persons. It is thus important to institute adequate safety measures in radiotherapy centres to prevent infection of cancer patients during the global Covid-19 pandemic. A survey conducted in 12 radiotherapy centres in 8 African countries has highlighted key measures needing implementation to ensure safety against Covid-19 infections. The safety measures were indexed on a 16-point questionnaire covering 5 main areas of staffing, radiotherapy environment, equipment and treatment protocols, patient condition and scheduling, and education/sensitization. The study shows that use of personal protective equipment, provision of hand washing and sanitizing facilities, social distance observance, restrictions for patient care-givers, provision of isolation unit meant for holding suspected Covid-19 cases, existence of working protocols, and Covid-19 safety education for staff are fully complied with by the surveyed radiotherapy centres. A greater portion of the centres, are however, without radiotherapy facilities solely dedicated for suspicious and confirmed Covid-19 cases. Strict adherence of the safety measures is highly essential to contain the spread and prevent infection of the disease to patients, care-givers and staff of the radiotherapy departments.

Automated treatment planning of postmastectomy radiotherapy

Purpose

Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT).

Methods

Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high‐ and low‐energy photon beams and field‐in‐field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three‐tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans.

Results

The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review.

Conclusions

We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments.

A risk assessment of automated treatment planning and recommendations for clinical deployment

Purpose

To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA.

Methods

We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four‐field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program.

Results

In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top‐ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician.

Conclusions

Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.

Model for Estimating Power and Downtime Effects on Teletherapy Units in Low-Resource Settings

Purpose

More than 6,500 megavoltage teletherapy units are needed worldwide, many in low-resource settings. Cobalt-60 units or linear accelerators (linacs) can fill this need. We have evaluated machine performance on the basis of patient throughput to provide insight into machine viability under various conditions in such a way that conclusions can be generalized to a vast array of clinical scenarios.

Materials and Methods

Data from patient treatment plans, peer-reviewed studies, and international organizations were combined to assess the relative patient throughput of linacs and cobalt-60 units that deliver radiotherapy with standard techniques under various power and maintenance support conditions. Data concerning the frequency and duration of power outages and downtime characteristics of the machines were used to model teletherapy operation in low-resource settings.

Results

Modeled average daily throughput was decreased for linacs because of lack of power infrastructure and for cobalt-60 units because of limited and decaying source strength. For conformal radiotherapy delivered with multileaf collimators, average daily patient throughput over 8 years of operation was equal for cobalt-60 units and linacs when an average of 1.83 hours of power outage occurred per 10-hour working day. Relative to conformal treatments delivered with multileaf collimators on the respective machines, the use of advanced techniques on linacs decreased throughput between 20% and 32% and, for cobalt machines, the need to manually place blocks reduced throughput up to 37%.

Conclusion

Our patient throughput data indicate that cobalt-60 units are generally best suited for implementation when machine operation might be 70% or less of total operable time because of power outages or mechanical repair. However, each implementation scenario is unique and requires consideration of all variables affecting implementation.

Comparison of Primary Doses Obtained in Three 6 MV Photon Beams Using a Small Attenuator

It is a common technique in radiotherapy treatment planning systems to simplify the calculations by splitting the radiation beam into two components: namely the primary and scattered components. The contributions of the two components are evaluated separately and then summed to give the dose at the point of interest. Usually, the primary dose is obtained experimentally by extrapolating the ionization measured within the medium to zero-field size (Godden, Gamma radiation from cobalt 60 teletherapy units. Br. J. Radiol. Suppl. , 45(1983)). This approach offers the opportunity to obtain the primary component of dose without the need for an uncertain non-linear extrapolation. The primary dose can be obtained from two measurements of ionization in a large beam in a water phantom, as well as four measurements of ionization in a narrow beam geometry. The measurements were done over a range of different depths and thus the primary linear attenuation coefficient was also obtained. The calibrated output of a linear accelerator is usually 1.00 Gy per 100 monitor units (MU) at the depth of maximum dose ( d max ) in water for a 10 cm × 10 cm field. The values for the primary dose components at d max in a 10 cm × 10 cm field obtained in three different 6 MV beams using this method are D P ( d max , 10 cm × 10 cm) = 0.925-0.943 Gy/100 MU. The obtained values of the primary dose components compare well with measurements in the same beams extrapolated to zero-field size and also to literature. One can thus conclude that this method has the potential to provide an independent measurable verification of calculations of primary dose.

Filtration to reduce paediatric dose for a linear slot-scanning digital X-ray machine

This paper describes modelling, application and validation of a filtration technique for a linear slot-scanning digital X-ray system to reduce radiation dose to paediatric patients while preserving diagnostic image quality. A dose prediction model was implemented, which calculates patient entrance doses using variable input parameters. Effective dose is calculated using a Monte Carlo simulation. An added filter of 1.8-mm aluminium was predicted to lower the radiation dose significantly. An objective image quality study was conducted using detective quantum efficiency (DQE). The PTW Normi 4FLU test phantom was used for quantitative assessment, showing that image contrast and spatial resolution were maintained with the proposed filter. A paediatric cadaver full-body imaging trial assessed the diagnostic quality of the images and measured the dose reduction using a 1.8-mm aluminium filter. Assessment by radiologists indicated that diagnostic quality was maintained with the added filtration, despite a reduction in DQE. A new filtration technique for full-body paediatric scanning on the Lodox Statscan has been validated, reducing entrance dose for paediatric patients by 36 % on average and effective dose by 27 % on average, while maintaining image quality.

Effect of aluminium filtration on dose and image quality in paediatric slot-scanning radiography

This paper examines the effect that a 1.8 mm aluminium filter has on paediatric patient dose and image quality for linear slot scanning radiography (LSSR). A dynamic dose prediction model for LSSR accurately predicted the dose reduction effects of added aluminium filtration. A cadaver imaging study was carried out to assess the effects of filtration on image quality. With 1.8 mm added aluminium filtration, no visible degradation to image contrast or clarity was found, and in some cases the aluminium filtration improved the image quality as judged by radiologists.