The promise of artificial intelligence-assisted radiotherapy for prostate cancer in Morocco: a transformational opportunity

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Prostate cancer is rapidly emerging as a significant public health concern in Morocco, with an incidence rate of 15 new cases per 100,000 men annually [1]. This escalating burden is placing considerable strain on the healthcare system, which is equipped with only approximately 80 linear accelerators to serve a population of 37 million.As one of the most frequently diagnosed cancers among Moroccan men, its management demands a highly precise approach, especially in radiotherapy, which remains a cornerstone for treating localized disease [2]. However, despite its proven effectiveness, traditional radiotherapy faces significant challenges such as inconsistent tumor delineation, variability in treatment planning, and the risk of radiation-induced toxicity to surrounding healthy tissues.These obstacles are even more pronounced in Morocco, where access to specialized radiotherapy services is still limited, particularly in rural areas where 40% of the population resides.AI is poised to transform prostate cancer treatment by improving radiotherapy precision [3]. AI algorithms enhance tumor segmentation, treatment planning, and response prediction, enabling more personalized care [4,5]. While deep learning models and ANNs show superior accuracy globally, concerns remain about their applicability to Moroccan and African populations, as many models are trained on Western datasets [6].The absence of locally validated AI solutions and standardized national radiotherapy guidelines for prostate cancer highlights the urgent need for context-specific research and tailored implementation strategies [7]. Morocco's "Plan Cancer 2020-2029" prioritizes technological innovation, creating a unique opportunity for AI integration. This article highlights the importance of integrating AI into prostate cancer radiotherapy in Morocco. It discusses AI's scientific principles, clinical applications, and challenges in a resource-limited healthcare system. Embracing AI can improve treatment accuracy, bridge gaps in cancer care, and enhance patient outcomes, making a strong case for its urgent implementation in the fight against prostate cancer.Tumor segmentation, the delineation of tumors and surrounding healthy tissues on medical images (e.g., CT, MRI), is a critical yet time-consuming and error-prone step in radiotherapy [8]. Accuracy in segmentation directly impacts treatment quality and patient outcomes. This challenge is particularly significant in Morocco, where a shortage of radiation oncology specialists further exacerbates the burden on the healthcare system. The AI-generated contours showed strong concordance with manual delineations, achieving DSCs of 0.82 for the prostate, 0.95 for the bladder, and 0.88 for the rectum. Additionally, a real-world validation study by Palazzo et al. demonstrated that AI-assisted contouring significantly reduced inter-observer variability and oncologist workload, reducing contouring time from 17-24 minutes manually to just 3-7 minutes with AI-assisted editing (p < 0.01) [11].In Morocco, where the shortage of radiation oncology specialists places immense pressure on the healthcare system, AI integration could be particularly impactful. Automating tumor delineation would not only alleviate the burden on specialists but also ensure more consistent and accurate contouring, reducing treatment delays and optimizing patient outcomes. Recent studies emphasize the need for locally validated AI models to account for regional anatomical variations and imaging protocols.Following tumor segmentation, treatment planning constitutes a critical step in the radiotherapy workflow. It involves determining the optimal radiation dose and beam configurations to achieve effective tumor control while minimizing exposure to surrounding healthy tissues. Traditionally, this process is complex, highly individualized, and dependent on manual adjustments by experienced dosimetrists and radiation oncologists. In resourceconstrained settings like Morocco, such workflows can be time-intensive (often 4-6 hours per case), inconsistent, and vulnerable to human error.Artificial Intelligence (AI), particularly Reinforcement Learning (RL), is emerging as a transformative solution to streamline and standardize treatment planning in both external beam radiotherapy (EBRT) and brachytherapy [12,13]. RL models learn through trial-anderror interactions with their environment, refining their strategies based on feedback to maximize treatment efficacy while minimizing toxicity [14].Planner (VTP) designed to optimize intensity-modulated radiation therapy (IMRT) plans for prostate cancer [15]. Using Q-learning and dose-volume histogram (DVH) inputs, the VTP autonomously adjusted dosimetric constraints to enhance plan quality [16]. A 2024 study validated this framework by applying DRL to volumetric modulated arc therapy (VMAT), achieving comparable target coverage (63.2 ± 0.6 Gy) while reducing the mean rectal dose by 17% compared to clinical plans. When integrated with the Eclipse treatment planning system, the VTP improved average plan scores from 6.18 to 8.14 across 50 testing cases [17].Recent developments have further improved training efficiency by 40% through the introduction of DVH-based embedding layers, enabling real-time adaptation to anatomical variability. In Moroccan settings where access to advanced planning technologies like IMRT may be limited , the integration of RL-based 3D-conformal planning tools could approximate high-quality dose distributions while reducing planning time to 1-2 hours. This technology not only improves consistency and quality but also democratizes access to advanced planning capabilities across diverse treatment centers [18,19].In parallel, AI applications in prostate brachytherapy are also demonstrating significant clinical promise. Low-dose-rate (LDR) brachytherapy is a highly targeted approach for localized prostate cancer but involves intricate planning to determine seed placement and dose distribution. Traditionally reliant on expert intervention, brachytherapy planning can be both time-consuming and variable.A Canadian study demonstrated that a machine learning (ML) algorithm could generate clinically equivalent LDR brachytherapy plans in just 0.84 minutes, compared to 17.88 minutes for expert-driven plans [20]. These AI-generated plans achieved comparable target coverage, organ-at-risk (OAR) sparing, and implant confidence, with only a 4% lower prostate V150% a non-significant difference. Expert reviewers were unable to distinguish between AI-generated and human-created plans [20,21].Further advances include the BRIGHT AI system, which automatically generates multiple near-optimal plans, allowing clinicians to select the best trade-off between tumor coverage and healthy tissue preservation [22]. The integration of deep reinforcement learning into brachytherapy workflows enables real-time constraint optimization and adaptive planning, supporting a synergistic relationship between human expertise and machine intelligence.Despite the automation potential, human oversight remains essential to balance clinical nuances and anatomical variability, especially when navigating trade-offs between target dose escalation and rectal or urethral sparing. In resource-limited contexts, AI can significantly reduce clinician workload while ensuring high-quality, personalized treatment planning, even in the absence of highly specialized staff [23].In Morocco, the integration of AI-driven planning tools across both EBRT and brachytherapy presents an unprecedented opportunity to enhance care equity, efficiency, and precision. RLbased systems can standardize workflows, reduce planning times by over 60%, and elevate the overall quality of radiotherapy services, especially in institutions lacking full-time medical physicists or IMRT infrastructure. AI thus offers not just automation, but augmentation of clinical expertise, ultimately improving access to safe and effe

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