Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients

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Sinzinger, FabianKTH,Medicinsk avbildning (author)

Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients

Article/chapterEnglish2022

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2022-04-27
Frontiers Media SA,2022
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LIBRIS-ID:oai:DiVA.org:kth-313029
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313029URI
https://doi.org/10.3389/fonc.2022.870457DOI
http://kipublications.ki.se/Default.aspx?queryparsed=id:149584413URI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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QC 20220601
ObjectiveSurvival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features. MethodsIn the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model. ResultsThe proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 +/- 0.03 vs. 0.62 +/- 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively. DiscussionThe experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Oftalmologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Ophthalmology hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Reproduktionsmedicin och gynekologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Obstetrics, Gynaecology and Reproductive Medicine hsv//eng
lung cancer
tumor segmentation
spherical convolutional neural network
survival rate prediction
deep learning
Cox Proportional Hazards
DeepSurv

Added entries (persons, corporate bodies, meetings, titles ...)

Astaraki, Mehdi,PhD Student,1984-Karolinska Institutet,KTH,Medicinsk avbildning,Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, Swede, Stockholm, Sweden.(Swepub:kth)u1usc61v (author)
Smedby, Örjan,Professor,1956-KTH,Medicinsk avbildning(Swepub:kth)u1vc2uzb (author)
Moreno, Rodrigo,1973-KTH,Medicinsk avbildning(Swepub:kth)u1osc58y (author)
KTHMedicinsk avbildning (creator_code:org_t)

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