The Role of Artificial Intelligence in Enhancing Precision Medicine for NSCLC – CancerNetwork

Introduction

Non–small cell lung cancer (NSCLC) is the most prevalent form of lung cancer globally and a leading cause of cancer-related deaths. Addressing this critical health challenge aligns with the Sustainable Development Goal (SDG) 3: Good Health and Well-being, which aims to reduce premature mortality from non-communicable diseases through prevention and treatment. Innovative approaches, particularly the integration of artificial intelligence (AI) in precision medicine, are essential to improve patient outcomes. AI technologies, including machine learning and deep learning models, are advancing diagnosis, treatment, and management of NSCLC by providing clinicians with more accurate and accessible data, thus supporting timely and personalized care.

AI-Driven Diagnostic Tools

Imaging Analysis

AI enhances imaging techniques such as low-dose CT scans, PET-CT, and chest radiographs by analyzing extensive imaging datasets with high precision. This capability supports SDG 3 by improving early detection and accurate diagnosis, which are vital for effective treatment. AI systems can differentiate benign from malignant lesions and predict genetic mutations from radiographic features, facilitating personalized medicine.

• Tan et al. demonstrated AI models predicting EGFR mutations and ALK rearrangement status with high accuracy (AUCs of 0.897 and 0.995 respectively).
• Wang et al. developed a multitask AI system predicting EGFR mutation and PD-L1 expression with AUCs of 0.842 and 0.799 using CT images.

These advancements may reduce the need for invasive pathologic diagnosis, enabling earlier treatment initiation and contributing to equitable healthcare access, supporting SDG 10: Reduced Inequalities.

Histopathologic Diagnosis

AI also aids in histologic classification and genetic mutation profiling through digital whole slide imaging, enhancing diagnostic accuracy and efficiency.

• Coudray et al. showed a neural network classifying lung adenocarcinoma and squamous cell carcinoma with an average AUC of 0.97, comparable to pathologists.
• The same study reported AI predicting mutations in genes such as EGFR and KRAS with AUCs between 0.733 and 0.856.

These tools support SDG 9: Industry, Innovation, and Infrastructure by fostering technological innovation in healthcare diagnostics.

Predictive Modeling for Treatment Response

Treatment Response Prediction

AI models provide individualized prognostic data, enhancing personalized treatment plans and patient empowerment, in line with SDG 3.

• Peng et al. developed a deep learning model predicting response to concurrent chemoradiotherapy with AUCs of 0.86 (training) and 0.84 (validation).

Such predictive capabilities enable patients to make informed decisions and improve treatment efficacy.

Prognostic Assessments

AI advances survival prediction and recurrence risk assessment, facilitating tailored follow-up and treatment strategies.

• Koyama et al. created a personalized survival prediction model using clinical and radiomics features, accurately forecasting outcomes in advanced NSCLC.
• Kim et al. developed a deep learning model predicting recurrence risk in lung adenocarcinoma with an AUC of 0.763, identifying high-risk patients for aggressive treatment.
• Kinoshita et al. designed an AI prognostic model using clinicopathological and blood test data, achieving high predictive accuracy for disease-free, overall, and cancer-specific survival.

These innovations contribute to SDG 3 by improving survival rates and quality of care.

Clinical Decision Support Systems

Real-Time Guidance

AI-powered systems enhance surgical and radiotherapy procedures, improving safety and efficacy, which supports SDG 3 and SDG 9.

• The Adaptive Radiotherapy Clinical Decision Support (ARCliDS) system optimizes radiotherapy dosages using patient-specific data and reinforcement learning, improving tumor control and reducing side effects.
• AI integration with near-infrared imaging assists surgeons in real-time tissue differentiation, enabling precise resection margins and minimizing complications.
• AI-enhanced indocyanine green perfusion analysis guides dissection planes to ensure complete tumor removal while preserving critical structures.
• Postoperative AI systems like MySurgeryRisk predict complications using electronic health records, allowing timely interventions and personalized follow-up care.

Challenges and Future Directions

Despite the transformative potential of AI in NSCLC management, challenges remain that must be addressed to fully realize benefits aligned with SDG 16: Peace, Justice, and Strong Institutions, emphasizing ethical governance and transparency.

1. Data quality and availability: High-quality, well-annotated datasets are essential for accurate and generalizable AI models.
2. Model interpretability: Enhancing transparency is critical to build clinician trust and facilitate adoption.
3. Ethical considerations: Addressing algorithmic bias, data security, and patient privacy requires stringent regulatory oversight.

AI is expected to serve as an adjunct to clinical expertise, augmenting the capabilities of healthcare professionals to provide personalized and effective care.

Conclusion

AI significantly advances precision medicine in NSCLC by improving diagnostic accuracy, treatment prediction, and personalized therapeutic strategies, thereby contributing to the achievement of SDG 3. AI-driven genetic profiling, radiomics, and clinical decision support systems have the potential to revolutionize patient care. However, addressing challenges related to data quality, interpretability, and ethics is crucial. Future progress depends on continued AI research and collaboration between clinicians and data scientists to harness AI’s full potential in NSCLC management, promoting health equity and innovation consistent with the Sustainable Development Goals.

1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted in the Article

1. SDG 3: Good Health and Well-being
   • The article focuses on improving diagnosis, treatment, and management of non-small cell lung cancer (NSCLC), which directly relates to ensuring healthy lives and promoting well-being for all ages.
   • AI-driven precision medicine aims to reduce mortality and improve patient outcomes, aligning with SDG 3’s goal to reduce premature mortality from non-communicable diseases.
2. SDG 9: Industry, Innovation, and Infrastructure
   • The development and application of AI technologies in medical diagnostics and treatment represent innovation and advancement in infrastructure and industry.
   • The article discusses AI models, machine learning, and deep learning as transformative tools in healthcare, supporting innovation.
3. SDG 17: Partnerships for the Goals
   • The article emphasizes collaboration between clinicians and data scientists to fully leverage AI’s potential, reflecting the importance of partnerships for sustainable development.

2. Specific Targets Under Those SDGs Identified Based on the Article’s Content

1. SDG 3: Good Health and Well-being
   • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
   • Target 3.b: Support the research and development of vaccines and medicines for communicable and non-communicable diseases that primarily affect developing countries, and provide access to affordable essential medicines and vaccines.
2. SDG 9: Industry, Innovation, and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including health technology, through increased research and development.
3. SDG 17: Partnerships for the Goals
   • Target 17.6: Enhance North-South, South-South, and triangular regional and international cooperation on and access to science, technology, and innovation.
   • Target 17.8: Fully operationalize the technology bank and science, technology, and innovation capacity-building mechanism for least developed countries.

3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets

1. Indicators for SDG 3 (Good Health and Well-being)
   • Mortality rate from non-communicable diseases (NCDs) such as lung cancer: The article discusses lung cancer mortality as a key issue.
   • Survival rates and recurrence-free survival: AI models predicting survival outcomes and recurrence risk (e.g., AUC scores for survival prediction and recurrence risk) serve as measures of treatment effectiveness.
   • Accuracy of diagnostic tools: AI model performance metrics such as Area Under the Curve (AUC) values for mutation prediction and treatment response prediction indicate improvements in diagnosis and personalized treatment.
   • Postoperative complication rates: AI prediction of complications like acute kidney injury and neurological complications with specific AUC values.
2. Indicators for SDG 9 (Industry, Innovation, and Infrastructure)
   • Number and performance of AI-driven diagnostic and predictive models: The article cites multiple AI models with quantified performance metrics (AUC scores), indicating technological advancement.
   • Implementation of AI clinical decision support systems: Use of systems like ARCliDS and MySurgeryRisk as indicators of innovation adoption in healthcare.
3. Indicators for SDG 17 (Partnerships for the Goals)
   • Collaborative research outputs: The article references multiple studies and collaborations between clinicians and data scientists, implying indicators related to joint research efforts.
   • Integration of AI tools in clinical practice: The extent to which AI tools are adopted and integrated into healthcare systems as a measure of successful partnerships and technology transfer.

4. Table: SDGs, Targets and Indicators

SDGs	Targets	Indicators
SDG 3: Good Health and Well-being	3.4: Reduce premature mortality from NCDs through prevention and treatment. 3.b: Support R&D of medicines and provide access to essential medicines.	Lung cancer mortality rates. Survival and recurrence-free survival rates predicted by AI models. Diagnostic accuracy metrics (AUC scores) for AI models predicting genetic mutations and treatment response. Postoperative complication prediction accuracy (AUC values for acute kidney injury, neurological complications).
SDG 9: Industry, Innovation, and Infrastructure	9.5: Enhance scientific research and upgrade technological capabilities.	Performance metrics (AUC scores) of AI diagnostic and predictive models. Adoption and effectiveness of AI clinical decision support systems (e.g., ARCliDS, MySurgeryRisk).
SDG 17: Partnerships for the Goals	17.6: Enhance international cooperation on science, technology, and innovation. 17.8: Operationalize technology banks and capacity-building mechanisms.	Collaborative research publications and studies involving clinicians and data scientists. Integration and use of AI tools in clinical practice as a measure of successful partnerships.

Source: cancernetwork.com