A Systematic Review of Radiotherapy Capacity in Low- and Middle-Income Countries

Grover, Surbhi; Xu, Melody J.; Yeager, Alyssa; Rosman, Lori; Groen, Reinou S.; Chackungal, Smita; Rodin, Danielle; Mangaali, Margaret J.; Nurkic, S.R.W.; Fernandes, A.; Lin, Lilie L.; Thomas, Gillian; Tergas, Ana I.

doi:10.3389/fonc.2014.00380

Cited by 98 publications

(84 citation statements)

References 53 publications

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“…Less than one-fourth of these cases will receive appropriate and safe surgery. Though many women present with advanced cervical cancer requiring chemoradiation therapy in LMICs, many resource-limited areas in Southeast Asia, Western Pacific Asia, Africa, and Latin America lack adequate radiotherapy and chemotherapy capabilities and therefore surgery may continue to play an important role in cervical cancer management (Abdel-Wahab et al, 2013, Grover et al, 2015). In particular, surgical management may play a key role in patients with early cervical cancer or locally advanced cervical cancer after neoadjuvant chemotherapy (Chuang et al, 2016).…”

Section: Surgery and Clinical Trials In Lmicsmentioning

confidence: 99%

Clinical trials in low and middle-income countries — Successes and challenges

Grover

Jhingran

et al. 2017

Gynecologic Oncology Reports

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Gynecologic malignancies affect women in low and middle-income countries (LMICs) at equal or higher rates compared to high income countries (HICs), yet practice guidelines based on clinical trials performed in HICs do not routinely account for resource disparities between these regions. There is a need and growing interest for executing clinical trials in LMICs. This has led to the creation of multinational cooperative groups and the initiation of several ongoing clinical trials in Mexico, China, and Korea. In this article we describe the challenges involved in initiating clinical trials in LMICs, review current efforts within surgical, medical, and radiation oncology, and introduce high priority topics for future research.

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Section: Surgery and Clinical Trials In Lmicsmentioning

confidence: 99%

Clinical trials in low and middle-income countries — Successes and challenges

Grover

Jhingran

et al. 2017

Gynecologic Oncology Reports

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“…The number, age, and quality of machines contribute to suboptimal RT capacity with many countries relying on machines that are more than 20 years old, which brings their functionality and reliability into question 27,29 . These issues all relate to a lack of adequate funding, often based on the lack of appropriate NCCPs.…”

Section: Funding For Up-to-date Equipmentmentioning

confidence: 99%

“…In a systematic review of the published literature, Grover et al 27 Radiation Therapists, all to be trained. The second Call for Action in this report is a target of increasing RT capacity by 25% by 2025.…”

Section: Adequate Facilitiesmentioning

confidence: 99%

“…While the growing cancer divide is becoming increasingly recognized 1,26,28,30,40,75 the solution in support of reducing the divide is very complex since it involves multiple social, economic, cultural and political facets. , Approaches to improving the availability of safe and effective RT in LMICs, will require collaboration between multiple organizations including local governments, regional, international as well as non-government organizations 40,41 .…”

Section: Partnerships and Peer-to-peer Collaborationsmentioning

confidence: 99%

“…The most significant RT challenges in LMICs include the quality and quantity of physical resources, the scarceness of skilled human resources, and the unequal distribution of available resources 1,27,28 . The number, age, and quality of machines contribute to suboptimal RT capacity with many countries relying on machines that are more than 20 years old, which brings their functionality and reliability into question 27,29 .…”

Section: Funding For Up-to-date Equipmentmentioning

confidence: 99%

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Radiation Oncology Quality and Safety Considerations in Low-Resource Settings: A Medical Physics Perspective

Dyk

Meghzifene

2017

Seminars in Radiation Oncology

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The last few years have seen a significant growth of interest in the global radiation therapy crisis.Various organizations have quantified the need and are providing aid in support of addressing the shortfalls existing in many low-to-middle income countries (LMICs). With the tremendous demand for new facilities, equipment and personnel, it is very important to recognize the quality and safety challenges and to address them directly. An examination of publications on quality and safety in radiation therapy indicates a consistency in a number of the recommendations; however, these authoritative reports were generally based on input from high-resourced contexts.Here we review these recommendations with a special emphasis on issues that are significant in LMICs. While multidimensional, training and staffing are top priorities; any support provided to lower resourced settings must address the numerous facets associated with quality and safety indicators. Strong partnerships between high-income and other countries will enhance the development of safe and resource-appropriate strategies for advancing the radiation treatment process. The real challenge is the engagement of a strong spirit of cooperation, collaboration and communication between the multiple organizations in support of reducing the cancer divide and improving the provision of safe and effective radiation therapy.

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Use of Crowd Innovation to Develop an Artificial Intelligence–Based Solution for Radiation Therapy Targeting

et al. 2019

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IMPORTANCE Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial training and is subject to significant interobserver variation. OBJECTIVE To determine whether crowd innovation could be used to rapidly produce artificial intelligence (AI) solutions that replicate the accuracy of an expert radiation oncologist in segmenting lung tumors for RT targeting. DESIGN, SETTING, AND PARTICIPANTS We conducted a 10-week, prize-based, online, 3-phase challenge (prizes totaled $55 000). A well-curated data set, including computed tomographic (CT) scans and lung tumor segmentations generated by an expert for clinical care, was used for the contest (CT scans from 461 patients; median 157 images per scan; 77 942 images in total; 8144 images with tumor present). Contestants were provided a training set of 229 CT scans with accompanying expert contours to develop their algorithms and given feedback on their performance throughout the contest, including from the expert clinician. MAIN OUTCOMES AND MEASURES The AI algorithms generated by contestants were automatically scored on an independent data set that was withheld from contestants, and performance ranked using quantitative metrics that evaluated overlap of each algorithm's automated segmentations with the expert's segmentations. Performance was further benchmarked against human expert interobserver and intraobserver variation. RESULTS A total of 564 contestants from 62 countries registered for this challenge, and 34 (6%) submitted algorithms. The automated segmentations produced by the top 5 AI algorithms, when combined using an ensemble model, had an accuracy (Dice coefficient = 0.79) that was within the benchmark of mean interobserver variation measured between 6 human experts. For phase 1, the top 7 algorithms had average custom segmentation scores (S scores) on the holdout data set ranging from 0.15 to 0.38, and suboptimal performance using relative measures of error. The average S scores for phase 2 increased to 0.53 to 0.57, with a similar improvement in other performance metrics. In phase 3, performance of the top algorithm increased by an additional 9%. Combining the top 5 algorithms from phase 2 and phase 3 using an ensemble model, yielded an additional 9% to 12% improvement in performance with a final S score reaching 0.68. CONCLUSIONS AND RELEVANCE A combined crowd innovation and AI approach rapidly produced automated algorithms that replicated the skills of a highly trained physician for a critical task in radiation therapy. These AI algorithms could improve cancer care globally by transferring the skills of expert clinicians to under-resourced health care settings.

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A Systematic Review of Radiotherapy Capacity in Low- and Middle-Income Countries

Cited by 98 publications

References 53 publications

Clinical trials in low and middle-income countries — Successes and challenges

Clinical trials in low and middle-income countries — Successes and challenges

Radiation Oncology Quality and Safety Considerations in Low-Resource Settings: A Medical Physics Perspective

Use of Crowd Innovation to Develop an Artificial Intelligence–Based Solution for Radiation Therapy Targeting

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