Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Complication Avoidance of Reconstruction Implant Radiation Therapy (CARIT): A Retrospective Case-Cohort Analysis

PURPOSE/OBJECTIVE(S)

Patients are increasingly offered implant-based reconstruction prior to radiotherapy. However, it is unclear if the radiation treatment technique itself impacts upon toxicity. We performed this study to compare outcomes following implant-based reconstruction amongst breast cancer patients treated with a standard radiotherapy technique that irradiates the entire chest wall versus a novel technique which uses a smaller clinical target volume (CTV) to spare the implant. Need for corrective surgery, capsular contracture, and cosmetic outcomes were evaluated, with the hypothesis that the novel technique would result in fewer adverse outcomes and less need for corrective surgery.

MATERIALS/METHODS

A retrospective case-cohort analysis of 57 patients who had post-mastectomy, implant-based reconstruction was conducted. Patients with invasive mammary carcinoma (IMC) or ductal carcinoma in-situ (DCIS) who were treated either with the novel radiotherapy technique (n = 26) or standard PMRT (n = 31) were included. Patient demographics such as age, BMI, TNM stage, implant size, hormone receptor status, and radiation course was collected. Primary endpoint was the need for corrective surgery within two years and cosmetic outcomes, measured using the Baker Classification Scale for capsular contracture and the Modified Harvard Harris Cosmetic Scale. Secondary endpoint was radiation-induced toxicity measured using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE). Cosmesis and toxicity were evaluated at 3 months post-radiation, 1 year, and after 13 months. Unpaired t-tests were used to compare need for corrective surgery, cosmesis (Baker Grade 2 or higher and Harvard Harris "Good" or worse), and toxicity (NCI CTCAE Grade 2 or higher).

RESULTS

For the primary endpoint, need for corrective surgery, there was no significant difference between the novel and standard groups (two-sided p = 0.378, CI -0.38-0.14). The other primary endpoint of cosmesis, measured with the Baker scale and Harvard Harris, was also not significantly different (two-sided p = 0.147, CI -0.06-0.45), with the Harvard Harris cosmesis differences remaining insignificant across the 3 month, 1 year, and greater than 13 month periods (two-sided p = 0.854, 0.351, 0.468, respectively). The secondary endpoint, toxicity, was not significantly different between the novel and standard PMRT groups across 3 months and 1 year time periods (two-sided p = 0.328, 0.323, respectively). We will also be reporting the analysis for predictive factors related to toxicity, need for corrective surgery and cosmesis.

CONCLUSION

Compared with standard PMRT, the novel technique was not significantly different in rates of reoperation, toxicity and cosmetic outcomes. Better understanding the factors involved in PMRT outcomes for breast cancer patients with implant-based reconstructions will aid in the development of standardized approaches to treating the breast cancer patient population.