Ramya R Iyer, Rajesh Sethuraman
Role of ehealth literacy, learning styles, and patterns of web-based e-content access for seeking health information among dental university students in Vadodara, India
Journal of education and health promotion
3d neural network dose predictions useful automating brachytherapy bt treatment planning cervical cancer cervical bt delivered numerous applicators necessitates developing models generalize multiple applicator types variability scarcity data given applicator type poses challenges deep learning goal work compare three methods neural network traininga single model trained applicator data finetuning combined model applicator individual idv applicator modelsto determine optimal method dose prediction models produced four applicator typestandemandovoid 17 needles ton tandemandring tr tr 14 needles trn first combined model trained 859 treatment plans 266 cervical cancer patients treated 2010 onwards trainvalidationtest split 701614 approximately 49101922 totontrtrn dataset inputs included four channels anatomical masks highrisk clinical target volume hrctv bladder rectum sigmoid mask indicating dwell position locations applicator channels applicator component applicator channels created mapping 3d dose single dwell position dwell position summing applicator component uniform dwell time weighting 3d cascade unet consists two unets sequence mean squared error loss function used combined model finetuned produce four applicatorspecific models freezing first unet encoding layers second resuming training applicatorspecific data finally four idv models trained using data applicator type performance three model types compared using following metrics test set mean error representing model bias mean absolute error mae dose voxels clinical metrics hrctv d90 d2cc bladder rectum sigmoid averaged patients positive indicates clinical dose higher predicted 3d global gamma analysis prescription dose reference value performed dice similarity coefficients dsc computed isodose volume finetuned combined models showed better performance idv applicator training finetuning resulted modest improvements half metrics compared combined model remainder mostly unchanged finetuned mae 398269536380 totrtontrn voxels 008089059142 d2cc bladder 077100066153 rectum 111022029337 sigmoid 047006237140 d90 26444800 gamma pass rates 33 mm 86918389 mean dscs 092092088091 isodoses 150 prescription 3d bt dose accurately predicted applicator types indicated low mae mes high gamma scores high dscs training treatment data overcomes challenges data scarcity applicator type resulting superior performance achieved training idv applicators alone could presumably explained fact larger diverse dataset allows neural network learn underlying trends characteristics dose common treatment applicators accurate applicatorspecific dose predictions could enable automated knowledgebased planning cervical brachytherapy treatment