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Saadatmand P, Mahdavi SR, Nikoofar A, Jazaeri SZ, Ramandi FL, Esmaili G, Vejdani S. A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac. Eur J Med Res 2024; 29:282. [PMID: 38735974 PMCID: PMC11089719 DOI: 10.1186/s40001-024-01855-y] [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: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. METHODS Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations. RESULTS Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. CONCLUSIONS This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.
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Affiliation(s)
- Pegah Saadatmand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Nikoofar
- Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seyede Zohreh Jazaeri
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Division of NeuroscienceCellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | | | - Soheil Vejdani
- Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Department of Radiation Oncology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
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Borghetti P, Costantino G, Santoro V, Mataj E, Singh N, Vitali P, Greco D, Volpi G, Sepulcri M, Guida C, Tomasi C, Buglione M, Nardone V. Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer. In Vivo 2024; 38:1359-1366. [PMID: 38688600 PMCID: PMC11059897 DOI: 10.21873/invivo.13576] [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: 10/03/2023] [Revised: 01/30/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND/AIM Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. PATIENTS AND METHODS Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. RESULTS Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. CONCLUSION The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.
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Affiliation(s)
- Paolo Borghetti
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | | | - Valeria Santoro
- Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy
| | - Eneida Mataj
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy;
| | - Navdeep Singh
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Paola Vitali
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Diana Greco
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Giulia Volpi
- Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy
| | - Matteo Sepulcri
- Radiotherapy Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Cesare Guida
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1, Naples, Italy
| | | | - Michela Buglione
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
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Huang K, Huang X, Zeng C, Wang S, Zhan Y, Cai Q, Peng G, Yang Z, Zhou L, Chen J, Chen C. Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy. Cancer Imaging 2024; 24:54. [PMID: 38654284 PMCID: PMC11036574 DOI: 10.1186/s40644-024-00680-0] [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: 11/23/2023] [Accepted: 02/28/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes. METHODS Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC). RESULTS The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004). CONCLUSION The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.
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Affiliation(s)
- Kang Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
- Department of Radiation Oncology, Zhongshan City People's Hospital, Zhongshan, P.R. China
| | - Xuehan Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
- Shantou University Medical College, Shantou, P.R. China
| | - Chengbing Zeng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
| | - Siyan Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
- Shantou University Medical College, Shantou, P.R. China
| | - Yizhou Zhan
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
| | - Qingxin Cai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
| | - Guobo Peng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China
| | - Li Zhou
- Department of Gynecologic Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Jianzhou Chen
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China.
- Gustave Roussy Cancer Campus, Villejuif Cedex, France.
- Institut National de la Santé Et de la Recherche Médicale (INSERM) U1015, Équipe Labellisée - Ligue Nationale contre le Cancer, Villejuif, France.
| | - Chuangzhen Chen
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, P.R. China.
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Huang BT, Wang Y, Lin PX. Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy. J Cancer Res Clin Oncol 2024; 150:34. [PMID: 38277078 PMCID: PMC10817845 DOI: 10.1007/s00432-023-05536-x] [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: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters. METHODS Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction. RESULTS With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735-0.914), 0.886 (95% CI 0.790-0.948), and 0.914 (95% CI 0.825-0.966) in the training cohort, and 0.757 (95% CI 0.580-0.887), 0.818 (95% CI 0.648-0.929), and 0.843 (95% CI 0.677-0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility. CONCLUSION A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.
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Affiliation(s)
- Bao-Tian Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China.
| | - Ying Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Pei-Xian Lin
- Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
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Lee S, Park S, Rim CH, Lee YH, Kwon SY, Oh KH, Yoon WS. A Potential Radiomics-Clinical Model for Predicting Failure of Lymph Node Control after Definite Radiotherapy in Locally Advanced Head and Neck Cancer. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:92. [PMID: 38256353 PMCID: PMC10821036 DOI: 10.3390/medicina60010092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/21/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024]
Abstract
Background and Objectives: To optimally predict lymph node (LN) failure after definite radiotherapy (RT) in head and neck cancer (HNC) with LN metastases, this study examined radiomics models extracted from CT images of different periods during RT. Materials and Methods: This study retrospectively collected radiologic and clinical information from patients undergoing definite RT over 60 Gy for HNC with LN metastases from January 2010 to August 2021. The same largest LNs in each patient from the initial simulation CT (CTpre) and the following simulation CT (CTmid) at approximately 40 Gy were indicated as regions of interest. LN failure was defined as residual or recurrent LN within 3 years after the end of RT. After the radiomics features were extracted, the radiomics alone model and the radiomics plus clinical parameters model from the set of CTpre and CTmid were compared. The LASSO method was applied to select features associated with LN failure. Results: Among 66 patients, 17 LN failures were observed. In the radiomics alone model, CTpre and CTmid had similar mean accuracies (0.681 and 0.697, respectively) and mean areas under the curve (AUC) (0.521 and 0.568, respectively). Radiomics features of spherical disproportion, size zone variance, and log minimum 2 were selected for CTpre plus clinical parameters. Volume, energy, homogeneity, and log minimum 1 were selected for CTmid plus clinical parameters. Clinical parameters including smoking, T-stage, ECE, and regression rate of LN were important for both CTpre and CTmid. In the radiomics plus clinical parameters models, the mean accuracy and mean AUC of CTmid (0.790 and 0.662, respectively) were more improved than those of CTpre (0.731 and 0.582, respectively). Conclusions: Both models using CTpre and CTmid were improved by adding clinical parameters. The radiomics model using CTmid plus clinical parameters was the best in predicting LN failure in our preliminary analyses.
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Affiliation(s)
- Seunghak Lee
- Core Research and Development Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea;
| | - Sunmin Park
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
| | - Chai Hong Rim
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
| | - Young Hen Lee
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea;
| | - Soon Young Kwon
- Department of Otolaryngology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea; (S.Y.K.); (K.H.O.)
| | - Kyung Ho Oh
- Department of Otolaryngology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea; (S.Y.K.); (K.H.O.)
| | - Won Sup Yoon
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
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Morelli L, Parrella G, Molinelli S, Magro G, Annunziata S, Mairani A, Chalaszczyk A, Fiore MR, Ciocca M, Paganelli C, Orlandi E, Baroni G. A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy. Cancers (Basel) 2022; 15:cancers15010033. [PMID: 36612029 PMCID: PMC9817801 DOI: 10.3390/cancers15010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan-Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LETd maps, with DRBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LETd maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Giovanni Parrella
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Giuseppe Magro
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Simone Annunziata
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Andrea Mairani
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
- Heidelberg Ion Beam Therapy Center (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
| | - Agnieszka Chalaszczyk
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Maria Rosaria Fiore
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Mario Ciocca
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Ester Orlandi
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Wang Y, Zhang Z, Mi X, Li M, Huang D, Song T, Qi X, Yang M. Elevation of effective p53 expression sensitizes wild-type p53 breast cancer cells to CDK7 inhibitor THZ1. Cell Commun Signal 2022; 20:96. [PMID: 36058938 PMCID: PMC9442925 DOI: 10.1186/s12964-022-00837-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/03/2022] [Indexed: 12/02/2022] Open
Abstract
Background The cyclin-dependent kinase 7 (CDK7) inhibitor THZ1 represses multiple cancer cells. However, its tumor-repressive efficiency in wild-type p53 breast cancer cells remains controversial. Methods We conducted various assays, including CCK8, colony formation, flow cytometry, western blotting, and lactate dehydrogenase release detection, to clarify whether p53 elevation sensitizes breast cancer cells to THZ1. Results We found that upregulating functional p53 contributes to the increased sensitivity of breast cancer cells to THZ1. Increased THZ1 sensitivity requires active p53 and an intact p53 pathway, which was confirmed by introducing exogenous wild-type p53 and the subsequent elevation of THZ1-mediated tumor suppression in breast cancer cells carrying mutant p53. We confirmed that p53 accumulates in the nucleus and mitochondria during cell death. Furthermore, we identified extensive transcriptional disruption, rather than solely CDK7 inhibition, as the mechanism underlying the nutlin-3 and THZ1-induced death of breast cancer cells. Finally, we observed the combined nutlin-3 and THZ1 treatment amplified gasdermin E cleavage. Conclusion Enhanced sensitivity of breast cancer cells to THZ1 can be achieved by increasing effective p53 expression. Our approach may serve as a potential treatment for patients with breast cancer resistant to regular therapies. Video Abstract
Supplementary Information The online version contains supplementary material available at 10.1186/s12964-022-00837-z.
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Affiliation(s)
- Yueyuan Wang
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Zhihao Zhang
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Xuguang Mi
- Tumor Biotherapy Center, Jilin Province People's Hospital, Changchun, 130021, Jilin, Republic of China
| | - Mingxi Li
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Dan Huang
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tingting Song
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Xiaoyan Qi
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Ming Yang
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
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Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach. Life (Basel) 2022; 12:life12091380. [PMID: 36143416 PMCID: PMC9505304 DOI: 10.3390/life12091380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/12/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes.
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Yang H, Wang L, Shao G, Dong B, Wang F, Wei Y, Li P, Chen H, Chen W, Zheng Y, He Y, Zhao Y, Du X, Sun X, Wang Z, Wang Y, Zhou X, Lai X, Feng W, Shen L, Qiu G, Ji Y, Chen J, Jiang Y, Liu J, Zeng J, Wang C, Zhao Q, Yang X, Hu X, Ma H, Chen Q, Chen M, Jiang H, Xu Y. A combined predictive model based on radiomics features and clinical factors for disease progression in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy. Front Oncol 2022; 12:967360. [PMID: 35982975 PMCID: PMC9380646 DOI: 10.3389/fonc.2022.967360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 07/05/2022] [Indexed: 12/04/2022] Open
Abstract
Purpose To accurately assess disease progression after Stereotactic Ablative Radiotherapy (SABR) of early-stage Non-Small Cell Lung Cancer (NSCLC), a combined predictive model based on pre-treatment CT radiomics features and clinical factors was established. Methods This study retrospectively analyzed the data of 96 patients with early-stage NSCLC treated with SABR. Clinical factors included general information (e.g. gender, age, KPS, Charlson score, lung function, smoking status), pre-treatment lesion status (e.g. diameter, location, pathological type, T stage), radiation parameters (biological effective dose, BED), the type of peritumoral radiation-induced lung injury (RILI). Independent risk factors were screened by logistic regression analysis. Radiomics features were extracted from pre-treatment CT. The minimum Redundancy Maximum Relevance (mRMR) and the Least Absolute Shrinkage and Selection Operator (LASSO) were adopted for the dimensionality reduction and feature selection. According to the weight coefficient of the features, the Radscore was calculated, and the radiomics model was constructed. Multiple logistic regression analysis was applied to establish the combined model based on radiomics features and clinical factors. Receiver Operating Characteristic (ROC) curve, DeLong test, Hosmer-Lemeshow test, and Decision Curve Analysis (DCA) were used to evaluate the model’s diagnostic efficiency and clinical practicability. Results With the median follow-up of 59.1 months, 29 patients developed progression and 67 remained good controlled within two years. Among the clinical factors, the type of peritumoral RILI was the only independent risk factor for progression (P< 0.05). Eleven features were selected from 1781 features to construct a radiomics model. For predicting disease progression after SABR, the Area Under the Curve (AUC) of training and validation cohorts in the radiomics model was 0.88 (95%CI 0.80-0.96) and 0.80 (95%CI 0.62-0.98), and AUC of training and validation cohorts in the combined model were 0.88 (95%CI 0.81-0.96) and 0.81 (95%CI 0.62-0.99). Both the radiomics and the combined models have good prediction efficiency in the training and validation cohorts. Still, DeLong test shows that there is no difference between them. Conclusions Compared with the clinical model, the radiomics model and the combined model can better predict the disease progression of early-stage NSCLC after SABR, which might contribute to individualized follow-up plans and treatment strategies.
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Affiliation(s)
- Hong Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Lin Wang
- Shaoxing University School of Medicine, Shaoxing, China
| | - Guoliang Shao
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Baiqiang Dong
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Fang Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electric (GE) Healthcare, Hangzhou, China
| | - Pu Li
- Department of Radiation Physics, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Haiyan Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Wujie Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yao Zheng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yiwei He
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yankun Zhao
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xianghui Du
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiaojiang Sun
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhun Wang
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yuezhen Wang
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xia Zhou
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiaojing Lai
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Wei Feng
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Liming Shen
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Guoqing Qiu
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yongling Ji
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jianxiang Chen
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Youhua Jiang
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jinshi Liu
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jian Zeng
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Changchun Wang
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Qiang Zhao
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xun Yang
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiao Hu
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Honglian Ma
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Qixun Chen
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Ming Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Haitao Jiang, ; Yujin Xu,
| | - Yujin Xu
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Haitao Jiang, ; Yujin Xu,
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Peng D, Yu DL, Liu XY, Tao W, Kang B, Zhang H, Wei ZQ, Ji GY. Does temporary ileostomy via specimen extraction site affect the short outcomes and complications after laparoscopic low anterior resection in rectal cancer patients? A propensity score matching analysis. BMC Surg 2022; 22:263. [PMID: 35799152 PMCID: PMC9264509 DOI: 10.1186/s12893-022-01715-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/04/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose The purpose of the current study was to compare the outcomes of temporary stoma through the specimen extraction site (SSES) and stoma through a new site (SNS) after laparoscopic low anterior resection. Methods The rectal cancer patients who underwent laparoscopic low anterior resection plus temporary ileostomy were recruited in a single clinical database from Jun 2013 to Jun 2020. The SSES group and the SNS group were compared using propensity score matching (PSM) analysis. Results A total of 257 rectal cancer patients were included in this study, there were 162 patients in the SSES group and 95 patients in the SNS group. After 1:1 ratio PSM, there was no difference in baseline information (p > 0.05). The SSES group had smaller intraoperative blood loss (p = 0.016 < 0.05), shorter operation time (p < 0.01) and shorter post-operative hospital stay (p = 0.021 < 0.05) than the SNS group before PSM. However, the SSES group shorter operation time (p = 0.006 < 0.05) than the SNS group after PSM, moreover, there was no significant difference in stoma-related complications (p > 0.05). In the multivariate analysis, longer operation time was an independent factor (p = 0.019 < 0.05, OR = 1.006, 95% CI = 1.001–1.011) for the stoma-related complications. Conclusion Based on the current evidence, the SSES group had smaller intraoperative blood loss, shorter operation time and shorter post-operative hospital stay before PSM, and shorter operation time after PSM. Therefore, SSES might be superior than SNS after laparoscopic low anterior resection for rectal cancer patients.
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Affiliation(s)
- Dong Peng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Dong-Ling Yu
- Department of General Surgery, Qijiang District People's Hospital, Chongqing, 401420, China
| | - Xiao-Yu Liu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wei Tao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Bing Kang
- Department of Clinical Nutrition, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Hua Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zheng-Qiang Wei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Guang-Yan Ji
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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