biostudies-literatureUnknown17(2)Salmanpour MR<h4>Objective</h4>This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs).<h4>Methods</h4>We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS-PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 40Cancers285https://www.ebi.ac.uk/biostudies/studies/S-EPMC11763441biostudies-literatureEnhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.PMC11763441Leung BFathi Jouzdani ASanati NGorji AYuan RHo CMousavi AMaghsudi MSalmanpour MRRahmim AfalseEnhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.<h4>Objective</h4>This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs).<h4>Methods</h4>We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS-PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 402025-01-01T00:00:00Z2025 Jan2025-04-18T14:27:43.523Z2025-04-07T00:37:32.461ZS-EPMC117634413985806710.3390/cancers17020285