Deep Learning Framework for Classifying Whole-Slide Multiplex Immunofluorescence Images to Predict Immunotherapy...

Area of Science:

• Oncology
• Computational Pathology
• Biomarker Discovery

Background:

• Immunotherapy is a key melanoma treatment, but patient response varies significantly.
• Predicting response is crucial for personalized medicine, yet current methods lack molecular insight.
• Whole-slide multiplex immunofluorescence (mIF) offers detailed tissue analysis but presents computational challenges.

Purpose of the Study:

• To develop a novel deep-learning framework for classifying whole-slide mIF images.
• To predict immunotherapy response in melanoma patients using advanced image analysis.
• To improve the accuracy of treatment response prediction through tissue-based biomarkers.

Main Methods:

• Introduced Channel Optimisation with Multi-Instance Learning (COMIL), a deep-learning framework.
• Developed a feature extraction method modeling inter-channel relationships in mIF images.
• Implemented an optimized Multi-Instance Learning (MIL) framework at slide and instance levels.

Main Results:

• COMIL significantly improved classification performance on mIF images.
• Achieved substantial AUC increases of 28% on internal and 20% on external test sets.
• Outperformed baseline methods in predicting immunotherapy response.

Conclusions:

• The COMIL framework effectively analyzes complex mIF data for melanoma immunotherapy response prediction.
• Modeling inter-channel relationships and using MIL enhances whole-slide image analysis.
• This approach holds significant potential for advancing personalized melanoma treatment strategies.