Details

Autor(en) / Beteiligte

• Godson, Lucy
• Alemi, Navid
• Nsengimana, Jérémie
• Cook, Graham P.
• Clarke, Emily L.
• Treanor, Darren
• Bishop, D. Timothy
• Newton-Bishop, Julia
• Gooya, Ali
• Magee, Derek

Titel

Immune subtyping of melanoma whole slide images using multiple instance learning

Ist Teil von

• Medical image analysis, 2024-04, Vol.93, p.103097-103097, Article 103097

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into ‘high’ or ‘low immune’ subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into ‘high’ and ‘low immune’ subgroups with significantly different melanoma specific survival outcomes (log rank test, P< 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests. [Display omitted] •RNA analysis to classify melanomas into immune subgroups is costly and time-consuming.•We compare SOTA MIL methods, showing these subgroups can be identified using WSIs.•10x resolution input patches improve performance, through a balance of cellular detail and context.•A SSL ‘feature extractor’, trained with histopathology images, improves performance.•The classified high and low immune subgroups have differing survival outcomes.