Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer.
Kevin Michael BoehmEmily A AherneLora EllensonInes NikolovskiMohammed AlghamdiIgnacio Vàzquez-GarcíaDmitriy ZamarinKara Long RocheYing LiuDruv M PatelAndrew AukermanArfath PashaDoori RosePier SelenicaPamela I Causa AndrieuChris FongMarinela CapanuJorge Sergio Reis-FilhoRami VanguriHarini VeeraraghavanNatalie GangaiRamon SosaSamantha LeungAndrew McPhersonJianJiong Gaonull nullYulia LakhmanSohrab P ShahPublished in: Nature cancer (2022)
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
Keyphrases
- high grade
- contrast enhanced
- computed tomography
- low grade
- poor prognosis
- machine learning
- magnetic resonance imaging
- prognostic factors
- diffusion weighted
- big data
- positron emission tomography
- long non coding rna
- magnetic resonance
- pain management
- dna damage
- electronic health record
- dna repair
- dual energy
- diffusion weighted imaging
- minimally invasive
- artificial intelligence
- high resolution
- drinking water
- neoadjuvant chemotherapy
- deep learning
- squamous cell carcinoma
- oxidative stress
- pet ct
- image quality
- percutaneous coronary intervention
- surgical site infection