Rhanoui Maryem, Mikram Mounia, Amazian Kamelia, Ait-Abderrahim Abderrahim, Yousfi Siham, Toughrai Imane
Laboratory Health Systemic Process (P2S), UR4129, University Claude Bernard Lyon 1, University of Lyon, 69008 Lyon, France.
Meridian Team, LyRICA Laboratory, School of Information Sciences, Rabat 10100, Morocco.
J Imaging. 2024 Nov 21;10(12):297. doi: 10.3390/jimaging10120297.
Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients' quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications.
结直肠癌是一个重大的公共卫生问题，在全球范围内导致了显著的发病率和死亡率。结直肠癌的治疗通常会对患者的生活质量产生重大影响，这种影响会随时间和个体而有所不同。人工智能和机器学习技术的应用具有巨大潜力，通过提供有价值的见解来优化患者的治疗结果。在本文中，我们提出了一个多模态机器学习框架，用于预测不同时间阶段结直肠癌患者的生活质量指标，同时利用临床数据和计算机断层扫描图像。此外，我们确定了每个生活质量指标的关键预测因素，从而使临床医生能够做出更明智的治疗决策，并最终改善患者的治疗结果。我们的方法整合了来自多个来源的数据，提高了预测模型的性能。分析表明，某些指标的准确性有显著提高，在整合不同模态的数据后，韦克斯纳评分的准确率从24%提高到48%，肛门直肠超声评分从88%提高到96%。这些结果凸显了多模态学习在实际应用中提供有价值见解和改善患者护理的潜力。
