Yong Yin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Yong Yin has a total of 24 co-authors. Here are the top co-authors by number of shared publications:
- Changsheng Ma
Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
3 shared publications - Jian Zhu
Department of Radiation Oncology Physics & Technology Cancer Hospital of Shandong First Medical University Jinan China
2 shared publications - Baosheng Li
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
1 shared publication Chengqiang Li
Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong Province, China
1 shared publicationChengze Li
Manteia Technologies Co.,Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, 361001, China
1 shared publication- Dali Han
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China
1 shared publication
Latest Publications
- Open Access
- 27 December 2022
- Published by Springer Nature in Radiation Oncology
- Vol. 17 (1) , 1-9
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures
Purpose: To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. Methods: 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. Results: There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). Conclusion: We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.
- 27 December 2022
- Published by Springer Nature in Radiation Oncology
- Vol. 17 (1) , 1-9
Open AccessMachine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures
Purpose: To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. Methods: 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. Results: There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). Conclusion: We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.
- Open Access
- 11 November 2022
- Published by Elsevier in Radiotherapy and Oncology
- Vol. 177, 222-230
Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy
- 11 November 2022
- Published by Elsevier in Radiotherapy and Oncology
- Vol. 177, 222-230
Open AccessPatient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy
- 1 November 2021
- Published by Medknow in Journal of Cancer Research and Therapeutics
- Vol. 17 (5) , 1165-1171
Dosimetry study of nasopharyngeal carcinoma based on Halcyon accelerator fixed-field intensity-modulated radiation therapy
Objective: Halcyon accelerator applies the flattening filter (FF)-free mode instead of the lead gate and FF treatment mode for traditional C-type accelerators. We aimed at comparing and analyzing the quality and delivery of nasopharyngeal carcinoma (NPC) plans between Halcyon and VitalBeam (VB) accelerators in fixed-field intensity-modulated radiation therapy (IMRT). Methods: The IMRT plans for thirty patients with NPC who had received radiotherapy were optimized using the VB (Plan VB) and Halcyon (Plan H) accelerators. Quality assurance verification was then conducted. The dose coverage of the planning target volume (PTV) and organs at risk (OARs), monitor units (MUs), and delivery time were analyzed for each plan. Results: All PTV and OAR indexes of Plan H and Plan VB met the clinical requirements. In the exposure dose of bilateral optic nerves between Plan H and Plan VB, no difference was found. The maximum dose of the lens, brainstem, spinal cord were 1.13 Gy, 1.36 Gy, 1.35 Gy, 2.82 Gy lower than the plan using VB , and the mean dose of the parotid glands were 3.82 Gy, 5.56 Gy lower than the plan using VB respectively, and an insignificant difference was found in the brainstem (P > 0.05). The MU for Plan H (22.92 ± 1.58 Gy) was higher than that for Plan VB (19.69 ± 4.52 Gy), and the difference was significant (P < 0.05). Conclusions: The treatment plans designed by Halcyon can meet clinical requirements with better protection for OARs and show advantages over VB in the dosimetry of NPC IMRT plans.
- 1 November 2021
- Published by Medknow in Journal of Cancer Research and Therapeutics
- Vol. 17 (5) , 1165-1171
Dosimetry study of nasopharyngeal carcinoma based on Halcyon accelerator fixed-field intensity-modulated radiation therapy
Objective: Halcyon accelerator applies the flattening filter (FF)-free mode instead of the lead gate and FF treatment mode for traditional C-type accelerators. We aimed at comparing and analyzing the quality and delivery of nasopharyngeal carcinoma (NPC) plans between Halcyon and VitalBeam (VB) accelerators in fixed-field intensity-modulated radiation therapy (IMRT). Methods: The IMRT plans for thirty patients with NPC who had received radiotherapy were optimized using the VB (Plan VB) and Halcyon (Plan H) accelerators. Quality assurance verification was then conducted. The dose coverage of the planning target volume (PTV) and organs at risk (OARs), monitor units (MUs), and delivery time were analyzed for each plan. Results: All PTV and OAR indexes of Plan H and Plan VB met the clinical requirements. In the exposure dose of bilateral optic nerves between Plan H and Plan VB, no difference was found. The maximum dose of the lens, brainstem, spinal cord were 1.13 Gy, 1.36 Gy, 1.35 Gy, 2.82 Gy lower than the plan using VB , and the mean dose of the parotid glands were 3.82 Gy, 5.56 Gy lower than the plan using VB respectively, and an insignificant difference was found in the brainstem (P > 0.05). The MU for Plan H (22.92 ± 1.58 Gy) was higher than that for Plan VB (19.69 ± 4.52 Gy), and the difference was significant (P < 0.05). Conclusions: The treatment plans designed by Halcyon can meet clinical requirements with better protection for OARs and show advantages over VB in the dosimetry of NPC IMRT plans.
- Open Access
- 9 January 2015
- Published by Springer Nature in Radiation Oncology
- Vol. 10 (1) , 11
Gradient-based delineation of the primary GTV on FLT PET in squamous cell cancer of the thoracic esophagus and impact on radiotherapy planning
- 9 January 2015
- Published by Springer Nature in Radiation Oncology
- Vol. 10 (1) , 11
Open AccessGradient-based delineation of the primary GTV on FLT PET in squamous cell cancer of the thoracic esophagus and impact on radiotherapy planning