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Akbarzadeh F, Khoshgard K. Enhancement of the effect of novel targeted 5-aminolevulinic acid conjugated bismuth oxide nanoparticles-based photodynamic therapy by simultaneous radiotherapy on KB cells. Photodiagnosis Photodyn Ther 2024; 46:104025. [PMID: 38403143 DOI: 10.1016/j.pdpdt.2024.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
BACKGROUND Selective accumulation of photosensitizers into cancerous cells is one of the most important factors affecting photodynamic therapy (PDT) efficacy. 5-aminolevulinic acid (5-ALA) is the precursor of a strong photosensitizer, protoporphyrin-IX; but it has poor permeability into the cells. Folate receptors are overexpressed on the surface of many tumor cells. In the present study, folic acid (FA) and 5-ALA conjugated bismuth oxide nanoparticles were synthesized; and used in PDT, radiotherapy (RT), and concurrent PDT & RT against nasopharyngeal carcinoma (KB cell line). METHODS The KB cells were incubated with the synthesized nanoparticles (NPs) for 2 h; then illuminated using a custom-made LED lamp at the light dose of 26 J/cm2. Irradiation of the cells was carried out using X-ray 6 MV (2 Gy); and synergistic effect of the simultaneous RT and PDT treatments was evaluated using fractional product values. Efficacy of the treatments was determined using MTT and Caspase-3 enzyme activity assays. RESULTS Targeting of folic acid receptors enables the selective endocytosis of the conjugated NPs. RT results in the presence of Bi2O3 NPs showed a significant radiosensitizer potential of these NPs. Fractional product values of 1.49±0.05, 1.36±0.06, and 1.05±0.06 obtained in the presence of FA-5-ALA conjugated NPs, 5-ALA conjugated NPs, and in the absence of the NPs, respectively. Therefore, simultaneous RT and PDT in the presence of these conjugated NPs is superior to RT in the presence of the NPs. CONCLUSION Simultaneous PDT and RT in the presence of FA-5-ALA conjugated bismuth oxide NPs can be introduced as a promising therapeutic approach in controlling KB cancer cells.
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Affiliation(s)
- Fatemeh Akbarzadeh
- Students Research Committee, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Karim Khoshgard
- Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Sorkheh-Lizhe Blvd, Kermanshah, P.O.Box:1568, Iran.
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Jiang J, Zhai R, Kong F, Du C, Ying H. Nomograms containing body dose parameters for predicting survival in patients with nasopharyngeal carcinoma. Eur Arch Otorhinolaryngol 2024; 281:181-192. [PMID: 37552282 PMCID: PMC10764493 DOI: 10.1007/s00405-023-08173-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/31/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE To assess the impact of body dose on survival outcomes in nasopharyngeal carcinoma (NPC) patients and to create novel nomograms incorporating body dose parameters for predicting survival. METHODS 594 of non-metastasis NPC patients (training group, 396; validation group, 198) received intensity-modulated radiation therapy at our institution from January 2012 to December 2016. Patient characteristics, body dose parameters in dose-volume histogram (DVH) and hematology profiles were collected for predicting overall survival (OS) and progression-free survival (PFS). Nomograms for OS and PFS were developed using the selected predictors. Each nomogram was evaluated based on its C-index and calibration curve. RESULTS Body dose-based risk score for OS (RSOS), N stage, age, and induction chemotherapy were independent predictors for OS, with a C-index of 0.784 (95% CI 0.749-0.819) in the training group and 0.763 (95% CI 0.715-0.810) in the validation group for the nomogram. As for PFS, the most important predictors were the body dose-based risk score for PFS (RSPFS), N stage, and induction chemotherapy. C-index of PFS nomogram was 0.706 (95% CI 0.681-0.720) in the training group and 0.691 (95% CI 0.662-0.711) in the validation group. The two models outperformed the TNM staging system in predicting outcomes. CONCLUSIONS Body dose coverage is a useful predictor of prognosis in clinical routine patients. The novel nomograms integrating body dose parameters can precisely predict OS and PFS in NPC patients.
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Affiliation(s)
- Jianyun Jiang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Ruiping Zhai
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Fangfang Kong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Chengrun Du
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Hongmei Ying
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China.
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China.
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Ding J, Li Z, Lin Y, Huang C, Chen J, Hong J, Fei Z, Zhou Q, Chen C. Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma. Sci Rep 2023; 13:18167. [PMID: 37875498 PMCID: PMC10598204 DOI: 10.1038/s41598-023-44933-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/13/2023] [Indexed: 10/26/2023] Open
Abstract
To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC.
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Affiliation(s)
- Jianming Ding
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Zirong Li
- Manteia Technologies Co., Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China
| | - Yuhao Lin
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Chaoxiong Huang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Jiawei Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Jiabiao Hong
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China
| | - Zhaodong Fei
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China.
| | - Qichao Zhou
- Manteia Technologies Co., Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, China.
| | - Chuanben Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuma Road, FuzhouFujian, 350014, China.
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Kotevski DP, Smee RI, Vajdic CM, Field M. Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study. JCO Clin Cancer Inform 2023; 7:e2200128. [PMID: 36596211 DOI: 10.1200/cci.22.00128] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
PURPOSE There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia. MATERIALS AND METHODS Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively. RESULTS Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age. CONCLUSION Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.
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Affiliation(s)
- Damian P Kotevski
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Robert I Smee
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.,Department of Radiation Oncology, Tamworth Base Hospital, Tamworth, New South Wales, Australia
| | - Claire M Vajdic
- Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Matthew Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia.,South Western Sydney Cancer Services, NSW Health, Sydney, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
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