Fig. 1: Schematic outline of the study. | Nature Cancer

Fig. 1: Schematic outline of the study.

From: Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Fig. 1

a–d, Multiple data modalities were acquired through routine diagnostics to inform clinical decision making (a): pre-treatment CE-CT scans of the abdomen and pelvis (b), pre-treatment H&E-stained diagnostic biopsies (c) and HRD status inferred from hybridization capture-based targeted sequencing or clinical HRD-DDR gene panels (d). e, Integrated multimodal analyses by late fusion to stratify patients by overall survival. Created with BioRender.com. GLSZM-SAE, gray level size zone matrix small area emphasis; GLRLM-GLV, gray level run length matrix gray level variance; Var, variance; Nuc, nuclear; NGS, next-generation sequencing; LSTs, large-scale state transitions; NtAI, number of subchromosomal regions with allelic imbalance extending to the telomere; LOH, loss of heterozygosity.

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