Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy

Abstract

Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses while minimizing damage to healthy tissues. However, the significant heterogeneity in immune responses among patients complicates its clinical application, hindering outcome prediction and treatment personalization. Artificial intelligence (AI) offers a transformative solution by integrating multidimensional data such as immunomics, radiomics, and clinical features to decode complex immune patterns and predict individual therapeutic outcomes. This editorial explored the potential of AI to address immune response heterogeneity in low-dose radiation therapy and proposed an AI-driven framework for precision immunotherapy. While promising, challenges, including data standardization, model interpretability, and clinical validation, must be overcome to ensure successful integration into oncological practice.

Key Words: Low-dose radiation; Immune response; Heterogeneity; Artificial intelligence; Precision medicine; Immunotherapy; Radiomics

Core Tip: Artificial intelligence (AI) is revolutionizing low-dose radiation therapy by addressing immune response heterogeneity in patients with cancer. By integrating multidimensional datasets such as immunomics, radiomics, and clinical profiles, AI employs advanced machine learning to decode complex immune patterns and predict individualized therapeutic outcomes. This enables tailored treatment strategies that enhance antitumor efficacy while minimizing side effects. Thus, AI paves the way for precision oncology and enables customized treatments for each patient’s unique biological signature.

INTRODUCTION

Low-dose radiation (LDR) has emerged as a promising modality for cancer therapy because of its ability to modulate immune responses while minimizing damage to healthy tissues. Research has shown that LDR can enhance antitumor immunity by activating immune cells, such as natural killer cells and T cells, offering a complementary approach to immunotherapy[1]. A bibliometric analysis by Wang et al[2] highlighted the growing interest in this field, with annual publications on LDR-induced immune responses remaining steady at approximately 30 papers per year between 2004 and 2013, followed by a marked increase in the subsequent decade, culminating in a total of 1244 publications by 2023. Key research hotspots include the integration of LDR with immune checkpoint inhibitors (e.g., ipilimumab) and stereotactic body radiotherapy, reflecting its potential in combination therapies.

Despite these advances, the heterogeneity of immune responses to LDR across patients remains a critical challenge. Studies have revealed significant variability in immune cell activation, with factors such as genetic background, tumor microenvironment, and radiation parameters influencing therapeutic outcomes[3,4]. This variability complicates the prediction of therapeutic efficacy and hinders personalization of LDR-based treatments. Traditional statistical approaches, which often rely on population averages, fail to account for individual differences and limit the precision of clinical applications.

Artificial intelligence (AI) offers a transformative solution by leveraging its capacity to process complex multidimensional data. With applications in immunomics, radiomics, and clinical outcome prediction, AI can decode the heterogeneity of LDR-induced immune responses, paving the way for precision medicine[5,6]. This editorial examined immune response variability, highlighted potential AI solutions, and outlined future directions for its integration into LDR therapy.

THE CHALLENGE OF IMMUNE RESPONSE HETEROGENEITY IN LDR THERAPY

LDR therapy offers significant potential in cancer treatment owing to its immunostimulatory effects. However, the heterogeneity of patient responses presents a formidable barrier to its widespread adoption. Research shows that LDR activates immune components variably. For example, natural killer cell activation differs widely across individuals based on factors such as radiation dose and baseline immune status[7]. In contrast, T cell responses, including CD4+ and CD8+ subsets, are modulated by tumor type, genetic predispositions, and the local inflammatory milieu[8,9]. This variability results in inconsistent therapeutic outcomes, as some patients exhibit robust antitumor immunity post-LDR, whereas others experience minimal effects or in rare cases tumor progression due to immunosuppressive shifts[10]. Consequently, these disparities emphasize the need for personalized strategies in LDR therapy.

The implications of this heterogeneity extend beyond inconsistent efficacy and pose significant risks to treatment success. Without personalized assessments, patients may receive suboptimal LDR doses, reducing therapeutic benefits and potentially compromising outcomes[11]. Moreover, heterogeneous immune responses can undermine the effectiveness of LDR when used in combination therapies such as immune checkpoint inhibitors, as evidenced by variable results in trials combining LDR with anti-PD-1 agents[12]. Current research methods, which predominantly rely on cohort-level data, struggle to capture these individual variations, limiting their ability to predict and tailor treatment effectively. Thus, innovative tools are urgently required to enhance the precision and personalization of LDR therapies.

AI: A PARADIGM SHIFT IN LDR THERAPY

AI is poised to revolutionize LDR therapy by addressing the limitations of traditional approaches through its capacity to process and analyze vast, complex datasets. This capability enables AI to identify patterns in immune response heterogeneity that conventional methods overlook, thereby offering a pathway to overcome the unpredictability of patient outcomes in LDR treatment. For instance, a systematic review by AlOsaimi et al[13] demonstrated the efficacy of AI models in identifying prognostic and predictive biomarkers in lung cancer, underscoring their potential to decipher tumor-immune interactions and support personalized treatment.

By integrating multidimensional biological signals, such as cytokine profiles, immune cell dynamics, and tumor microenvironment features, AI can uncover patient-specific immune signatures that inform tailored therapeutic strategies[14]. For instance, studies have shown that AI models can correlate T cell infiltration levels with LDR outcomes, accurately identifying responders and enhancing treatment precision[15]. Beyond pattern recognition, AI facilitates individualized outcome predictions and real-time adjustments, marking a significant shift toward precision oncology. This transformative potential positions AI as a cornerstone for optimizing the clinical efficacy of LDR therapy. Ultimately, AI integration could help translate empirical insights into actionable, patient-specific treatments.

Emerging evidence indicates that immune responses to LDR vary considerably across tumor types, necessitating tumor-specific AI models. For example, in non-small cell lung cancer, PD-L1 expression and the spatial distribution of tumor-infiltrating lymphocytes have been shown to correlate with response to immunotherapy. A machine learning-based histological analysis of tumor-infiltrating lymphocytes can aid in identifying responders and refining LDR strategies guided by AI[16]. In colorectal cancer, immune evasion is frequently associated with microsatellite instability and low antigenicity in microsatellite-stable tumors. AI algorithms are being used to stratify these subtypes and optimize immunostimulatory interventions in combination with radiotherapy[17]. Renal cell carcinoma, particularly the clear cell subtype, is characterized by dense immune infiltration and angiogenesis. AI-based profiling of genomic and immune features enables prediction of response to immunotherapy and may guide LDR application in this vascularized tumor context[18]. Incorporating such distinctions into AI frameworks enhances predictive power and aligns LDR applications with tumor-specific biology.

AI holds strong potential to enhance LDR therapy by predicting immune responses, guiding treatment stratification, and enabling adaptive planning. Machine learning techniques can analyze baseline biomarkers, such as immune cell densities and cytokine levels, to forecast outcomes and identify responders with high precision[19]. AI also facilitates real-time monitoring of immune dynamics using data from wearable sensors or liquid biopsies, enabling adjustments in radiation dose or immunotherapy timing based on patient feedback[20]. Despite recent advances, several obstacles remain, including the scarcity of LDR-specific datasets and the difficulty in interpreting complex AI models[21]. Furthermore, clinical validation through prospective multicenter trials is essential to ensure the reliability and generalizability of AI-assisted decision-making[22]. The ongoing integration of radiomics, single-cell profiling, and explainable AI tools is expected to strengthen the role of AI in delivering precision LDR therapy.

CONCLUSION

The heterogeneity of immune responses to LDR therapy presents a significant challenge for its optimization in cancer treatment. AI offers a compelling solution by integrating multidimensional data to enable individualized outcome prediction and adaptive therapeutic strategies. Although obstacles such as data availability and model interpretability remain, the integration of AI with LDR nonetheless holds transformative potential for advancing precision oncology. Future efforts should prioritize standardized data collection, interpretable AI frameworks, and robust clinical trials to bridge the gap between research and practice. Harnessing AI could ultimately transform LDR therapy into a cornerstone of personalized cancer care.