1
|
Zhou C, Li H, Zeng H, Wang P. Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage. Clin Transl Oncol 2024:10.1007/s12094-024-03717-9. [PMID: 39333451 DOI: 10.1007/s12094-024-03717-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Accepted: 09/02/2024] [Indexed: 09/29/2024]
Abstract
OBJECTIVE The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to predict the likelihood of metastasis. METHODS Involving 2465 OSC and 1373 EWS patients aged 0-19 years, from 2004 to 2020. ML techniques-Lasso, Ridge Regression, Elastic Net, and Random Forest-were used alongside a deep learning model based on TensorFlow and Keras, to construct predictive models for metastasis. These models were optimized using grid search with cross-validation and evaluated on their performance metrics, including AUC, sensitivity, and accuracy. The variables' importance in metastasis prediction was determined using SHAP values. Statistical analysis was performed using R software, and an online nomogram was developed for clinical use. RESULTS The age-adjusted incidence of OSC and EWS from 2004 to 2020 showed a significant uptrend. The deep learning model, iterated 50 times, outperformed the Random Forest model in both loss and accuracy stabilization. The nomogram created demonstrated accurate survival predictions, as evidenced by its calibration curves and the distinction between high and low-risk groups. CONCLUSION The increasing trend in age-adjusted incidence of OSC and EWS highlights the need for continued research and improved therapeutic strategies in this domain. The study employed ML and DL models to predict distant metastasis in pediatric and adolescent patients with OSC and EWS, providing a valuable tool for prognosis. The online nomogram developed as a part of this research enhances the models' clinical utility, offering an accessible means for clinicians to predict survival outcomes effectively.
Collapse
Affiliation(s)
- Chengyuan Zhou
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China
| | - Han Li
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China
| | - Hao Zeng
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China
| | - Pan Wang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 25 TAIPING Street, Luzhou City, 646000, Sichuan Province, China.
| |
Collapse
|
2
|
Tang W, Li R, Lai X, Yu X, He R. Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis. Heliyon 2024; 10:e37013. [PMID: 39286090 PMCID: PMC11402751 DOI: 10.1016/j.heliyon.2024.e37013] [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: 04/04/2024] [Revised: 08/24/2024] [Accepted: 08/26/2024] [Indexed: 09/19/2024] Open
Abstract
Background This study aimed to develop and validate nomograms to predict overall survival (OS) for pelvic Ewing's sarcoma (EWS) and chordoma, identify prognostic factors, and compare outcomes between the two conditions. Methods We identified patients diagnosed with pelvic EWS or chordoma from the SEER database (2001-2019). Independent risk factors were identified using univariate and multivariate Cox regression analyses, and these factors were used to construct nomograms predicting 3-, 5-, and 10-year OS. Validation methods included AUC, calibration plots, C-index, and decision curve analysis (DCA). Kaplan-Meier curves and log-rank tests compared survival differences between low- and high-risk groups. Results The study included 1175 patients (EWS: 611, chordoma: 564). Both groups were randomly divided into training (70 %) and validation (30 %) cohorts. OS was significantly higher for chordoma. Multivariate analysis showed year of diagnosis, income, stage, and surgery were significant for EWS survival, while age, time to treatment, stage, and surgery were significant for chordoma survival. Validation showed the nomograms had strong predictive performance and clinical utility. Conclusions The nomograms reliably predict overall survival (OS) in pelvic EWS and chordoma, helping to identify high-risk patients early and guide preventive measures. The study also found that survival rates are significantly higher for chordoma, highlighting different prognostic profiles between EWS and chordoma.
Collapse
Affiliation(s)
- Wanyun Tang
- Department of Orthopedics, Zigong First People's Hospital, Zigong, China
| | - Runzhuo Li
- Department of Digestion,The First People's Hospital of Yibin, Yibin, China
| | - Xiaoying Lai
- Department of Orthopedics, Zigong First People's Hospital, Zigong, China
| | - Xiaohan Yu
- Department of General Surgery, Dandong Central Hospital, China Medical University, Dandong, China
| | - Renjian He
- Department of Orthopedics, Zigong First People's Hospital, Zigong, China
| |
Collapse
|
3
|
Jiang Q, Hu H, Liao J, Li ZH, Tan J. Development and validation of a nomogram for breast cancer-related lymphedema. Sci Rep 2024; 14:15602. [PMID: 38971880 PMCID: PMC11227568 DOI: 10.1038/s41598-024-66573-1] [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: 12/25/2023] [Accepted: 07/02/2024] [Indexed: 07/08/2024] Open
Abstract
To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.
Collapse
Affiliation(s)
- Qihua Jiang
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Hai Hu
- Department of General Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Jing Liao
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China
| | - Zhi-Hua Li
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China.
- Jiangxi Province Key Laboratory of Breast Diseases, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang City, 330008, Jiangxi Province, China.
| | - Juntao Tan
- Department of Breast Surgery, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xi Hu District, Nanchang City, 330008, Jiangxi Province, China.
- Jiangxi Province Key Laboratory of Breast Diseases, Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang City, 330008, Jiangxi Province, China.
| |
Collapse
|
4
|
Li Q, Fang J, Liu K, Luo P, Wang X. Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma. Front Immunol 2024; 15:1424806. [PMID: 38983852 PMCID: PMC11231095 DOI: 10.3389/fimmu.2024.1424806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/06/2024] [Indexed: 07/11/2024] Open
Abstract
Background The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells. Methods This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis. Results In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells. Conclusion The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.
Collapse
Affiliation(s)
- Qingbiao Li
- Department of Orthopedics, Southern Medical University Pingshan Hospital (Pingshan District Peoples’ Hospital of Shenzhen), Shenzhen, Guangdong, China
| | - Jiarui Fang
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Kai Liu
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Peng Luo
- Department of Sport Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, China
| | - Xiuzhuo Wang
- Department of Orthopedics, Southern Medical University Pingshan Hospital (Pingshan District Peoples’ Hospital of Shenzhen), Shenzhen, Guangdong, China
| |
Collapse
|
5
|
Yang Y, Zhu J, Feng R, Han M, Chen F, Hu Y. Altered vaginal cervical microbiota diversity contributes to HPV-induced cervical cancer via inflammation regulation. PeerJ 2024; 12:e17415. [PMID: 38881859 PMCID: PMC11179633 DOI: 10.7717/peerj.17415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/28/2024] [Indexed: 06/18/2024] Open
Abstract
Background Cancer has surpassed infectious diseases and heart ailments, taking the top spot in the disease hierarchy. Cervical cancer is a significant concern for women due to high incidence and mortality rates, linked to the human papillomavirus (HPV). HPV infection leads to precancerous lesions progressing to cervical cancer. The cervix's external os, near the vagina, hosts various microorganisms. Evidence points to the link between vaginal microbiota and HPV-induced cervical cancer. Cervical cancer onset aligns with an imbalanced Th1/Th2 immune response, but the role of vaginal microbiota in modulating this imbalance is unclear. Methods In this study, we collected vaginal samples from 99 HPV-infected patients across varying degrees of lesions, alongside control groups. These samples underwent bacterial DNA sequencing. Additionally, we employed Elisa kits to quantify the protein expression levels of Th1/Th2 cytokines IL2, IL12, IL5, IL13, and TNFa within the centrifuged supernatant of vaginal-cervical secretions from diverse research subjects. Subsequently, correlation analyses were conducted between inflammatory factors and vaginal microbiota. Results Our findings highlighted a correlation between decreased Lactobacillus and increased Gardenerella presence with HPV-induced cervical cancer. Functionally, our predictive analysis revealed the predominant enrichment of the ABC transporter within the vaginal microbiota of cervical cancer patients. Notably, these microbiota alterations exhibited correlations with the production of Th1/Th2 cytokines, which are intimately tied to tumor immunity. Conclusions This study suggests the potential involvement of vaginal microbiota in the progression of HPV-induced cervical cancer through Th1/Th2 cytokine regulation. This novel insight offers a fresh perspective for early cervical cancer diagnosis and future prevention strategies.
Collapse
Affiliation(s)
- Yiheng Yang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jufan Zhu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Renqian Feng
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mengfei Han
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Yan Hu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| |
Collapse
|
6
|
Wei Q, Lu X, Yang Z, Zhu J, Jiang J, Xu Y, Li F, Bu H, Chen Y, Tuo S, Chen R, Ye X, Geer L, Tan X, Wang J, Wu Y, Song F, Su Y. Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study. J Spinal Cord Med 2024:1-9. [PMID: 38656250 DOI: 10.1080/10790268.2024.2329437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN The study is a retrospective single-center study. PARTICIPANTS SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.
Collapse
Affiliation(s)
- Qian Wei
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xuefeng Lu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Zihong Yang
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Jichong Zhu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jie Jiang
- The Second Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yaobin Xu
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Fengxin Li
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Haifeng Bu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yikai Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Sijing Tuo
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Ruyu Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiaoxia Ye
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Laoyi Geer
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiuwei Tan
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jiling Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yanlan Wu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Fangming Song
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
| | - Yiji Su
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
| |
Collapse
|
7
|
Marjańska A, Pawińska-Wąsikowska K, Wieczorek A, Drogosiewicz M, Dembowska-Bagińska B, Bobeff K, Młynarski W, Adamczewska-Wawrzynowicz K, Wachowiak J, Krawczyk MA, Irga-Jaworska N, Węcławek-Tompol J, Kałwak K, Sawicka-Żukowska M, Krawczuk-Rybak M, Raciborska A, Mizia-Malarz A, Sobocińska-Mirska A, Łaguna P, Balwierz W, Styczyński J. Anti-PD-1 Therapy in Advanced Pediatric Malignancies in Nationwide Study: Good Outcome in Skin Melanoma and Hodgkin Lymphoma. Cancers (Basel) 2024; 16:968. [PMID: 38473329 DOI: 10.3390/cancers16050968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND/AIM The role of immune checkpoint inhibitors (ICIs; anti-PD1) in the treatment of childhood cancers is still evolving. The aim of this nationwide retrospective study was to assess the safety and effectiveness of ICIs used in a group of 42 patients, with a median age of 13.6 years, with various types of advanced malignancies treated in pediatric oncology centers in Poland between 2015 and 2023. RESULTS The indications for treatment with anti-PD1 were as follows: Hodgkin lymphoma (11); malignant skin melanoma (9); neuroblastoma (8); and other malignancies (14). At the end of follow-up, complete remission (CR) was observed in 37.7% (15/42) of children and disease stabilization in 9.5% (4/42), with a mean survival 3.6 (95% CI = 2.6-4.6) years. The best survival (OS = 1.0) was observed in the group of patients with Hodgkin lymphoma. For malignant melanoma of the skin, neuroblastoma, and other rare malignancies, the estimated 3-year OS values were, respectively, 0.78, 0.33, and 0.25 (p = 0.002). The best progression-free survival value (0.78) was observed in the group with malignant melanoma. Significantly better effects of immunotherapy were confirmed in patients ≥ 14 years of age and good overall performance ECOG status. Severe adverse events were observed in 30.9% (13/42) patients.
Collapse
Affiliation(s)
- Agata Marjańska
- Department of Pediatric, Hematology and Oncology, Jurasz University Hospital, Collegium Medicum, Nicolaus Copernicus University Toruń, 85-094 Bydgoszcz, Poland
| | | | - Aleksandra Wieczorek
- Department of Pediatric, Oncology and Hematology, Jagiellonian University Medical College, 30-663 Cracow, Poland
| | - Monika Drogosiewicz
- Department of Oncology, The Children's Memorial Health Institute, 04-730 Warsaw, Poland
| | | | - Katarzyna Bobeff
- Department of Pediatrics, Oncology and Hematology, Medical University of Łodz, 91-738 Łodz, Poland
| | - Wojciech Młynarski
- Department of Pediatrics, Oncology and Hematology, Medical University of Łodz, 91-738 Łodz, Poland
| | - Katarzyna Adamczewska-Wawrzynowicz
- Department of Pediatric Oncology, Hematology and Transplantology, Jonscher Clinical Hospital, Marcinkowski University of Medical Sciences in Poznań, 60-572 Poznań, Poland
| | - Jacek Wachowiak
- Department of Pediatric Oncology, Hematology and Transplantology, Jonscher Clinical Hospital, Marcinkowski University of Medical Sciences in Poznań, 60-572 Poznań, Poland
| | - Małgorzata A Krawczyk
- Department of Pediatrics, Hematology and Oncology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Ninela Irga-Jaworska
- Department of Pediatrics, Hematology and Oncology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Jadwiga Węcławek-Tompol
- Department of Bone Marrow Transplantation, Pediatric Oncology and Hematology, Mikulicz-Radecki University Clinical Hospital, 50-556 Wrocław, Poland
| | - Krzysztof Kałwak
- Department of Bone Marrow Transplantation, Pediatric Oncology and Hematology, Mikulicz-Radecki University Clinical Hospital, 50-556 Wrocław, Poland
| | | | - Maryna Krawczuk-Rybak
- Department of Pediatric Oncology and Hematology, Medical University of Białystok, 15-274 Białystok, Poland
| | - Anna Raciborska
- Department of Oncology and Surgical Oncology for Children and Youth, Institute of Mother and Child, 01-211 Warsaw, Poland
| | - Agnieszka Mizia-Malarz
- Department of Pediatric, Oncology, Hematology and Chemotherapy, Upper Silesia Children's Care Health Centre, Medical University of Silesia, 40-752 Katowice, Poland
| | - Agata Sobocińska-Mirska
- Department of Oncology, Children's Hematology, Clinical Transplantology and Pediatrics, University Clinical Center, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Paweł Łaguna
- Department of Oncology, Children's Hematology, Clinical Transplantology and Pediatrics, University Clinical Center, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Walentyna Balwierz
- Department of Pediatric, Oncology and Hematology, Jagiellonian University Medical College, 30-663 Cracow, Poland
| | - Jan Styczyński
- Department of Pediatric, Hematology and Oncology, Jurasz University Hospital, Collegium Medicum, Nicolaus Copernicus University Toruń, 85-094 Bydgoszcz, Poland
| |
Collapse
|
8
|
Qi W, Ren Y, Wang H, Wan Y, Wang D, Yao J, Pan H. Establishment and validation of nomogram models for overall survival and cancer-specific survival in spindle cell sarcoma patients. Sci Rep 2023; 13:23018. [PMID: 38155261 PMCID: PMC10754933 DOI: 10.1038/s41598-023-50401-z] [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: 07/14/2023] [Accepted: 12/19/2023] [Indexed: 12/30/2023] Open
Abstract
Spindle cell sarcoma (SCS) is rare in clinical practice. The objective of this study was to establish nomograms to predict the OS and CSS prognosis of patients with SCS based on the Surveillance, Epidemiology, and End Results (SEER) database. The data of patients with SCS between 2004 and 2020 were extracted from the SEER database and randomly allocated to a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to screen for independent risk factors for both overall survival (OS) and cancer-specific survival (CSS). Nomograms for OS and CSS were established for patients with SCS based on the results of multivariate Cox analysis. Then, we validated the nomograms by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Finally, Kaplan‒Meier curves and log-rank tests were applied to compare patients with SCS at three different levels and in different treatment groups. A total of 1369 patients with SCS were included and randomly allocated to a training cohort (n = 1008, 70%) and a validation cohort (n = 430, 30%). Age, stage, grade, tumour location, surgery, radiation and diagnosis year were found to be independent prognostic factors for OS by Cox regression analysis, while age, stage, grade, tumour location and surgery were found to be independent prognostic factors for CSS. The nomogram models were established based on the results of multivariate Cox analysis for both OS and CSS. The C-indices of the OS model were 0.76 and 0.77 in the training and validation groups, respectively, while they were 0.76 and 0.78 for CSS, respectively. For OS, the 3- and 5-year AUCs were 0.801 and 0.798, respectively, in the training cohort and 0.827 and 0.799, respectively, in the validation cohort; for CSS, they were 0.809 and 0.786, respectively, in the training cohort and 0.831 and 0.801, respectively, in the validation cohort. Calibration curves revealed high consistency in both OS and CSS between the observed survival and the predicted survival. In addition, DCA was used to analyse the clinical practicality of the OS and CSS nomogram models and revealed that they had good net benefits. Surgery remains the main treatment method for SCS patients. The two nomograms we established are expected to accurately predict the personalized prognosis of SCS patients and may be useful for clinical decision-making.
Collapse
Affiliation(s)
- Weihui Qi
- Department of Orthopaedics, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, People's Republic of China
- Department of Orthopaedics, Hangzhou Ding Qiao Hospital, Hangzhou, China
| | - Yanyun Ren
- Department of Stomatology, No. 903 Hospital of PLA, Hangzhou, China
| | - Huang Wang
- Department of Orthopaedics, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, People's Republic of China
- Department of Orthopaedics, Hangzhou Ding Qiao Hospital, Hangzhou, China
| | - Yue Wan
- Department of Stomatology, No. 903 Hospital of PLA, Hangzhou, China
| | - Dong Wang
- Department of Orthopaedics, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, People's Republic of China
- Department of Orthopaedics, Hangzhou Ding Qiao Hospital, Hangzhou, China
| | - Jun Yao
- Department of Orthopaedics, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, People's Republic of China.
- Department of Orthopaedics, Hangzhou Ding Qiao Hospital, Hangzhou, China.
| | - Hao Pan
- Department of Orthopaedics, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, People's Republic of China.
- Department of Orthopaedics, Hangzhou Ding Qiao Hospital, Hangzhou, China.
| |
Collapse
|
9
|
Liu Y, Xie L, Wang D, Xia K. A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. PLoS One 2023; 18:e0286841. [PMID: 37768965 PMCID: PMC10538762 DOI: 10.1371/journal.pone.0286841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/24/2023] [Indexed: 09/30/2023] Open
Abstract
OBJECTIVE Successful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC. METHODS Data on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME). RESULTS A total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients. CONCLUSIONS After validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.
Collapse
Affiliation(s)
- Yang Liu
- Department of Orthopedics, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Lang Xie
- Hospital Infection Management Department, Bijie First People's Hospital, Bijie, China
| | - Dingxue Wang
- Department of Oncology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Kaide Xia
- Clinical College of Maternal and Child Health Care, Guizhou Medical University, Guiyang, China
| |
Collapse
|
10
|
Hao Y, Liang D, Zhang S, Wu S, Li D, Wang Y, Shi M, He Y. Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort. BIOMOLECULES & BIOMEDICINE 2023; 23:883-893. [PMID: 36967662 PMCID: PMC10494842 DOI: 10.17305/bb.2023.8804] [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: 01/18/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 06/18/2023]
Abstract
Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China.
Collapse
Affiliation(s)
- Yahui Hao
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Di Liang
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Shuo Zhang
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Siqi Wu
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Daojuan Li
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Yingying Wang
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Miaomiao Shi
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| | - Yutong He
- Cancer Institute, The Fourth Hospital of Hebei Medical University/The Tumor Hospital of Hebei Province, Shijiazhuang, China
| |
Collapse
|
11
|
Chen W, He X, Yan Z, Lin X, Bai G. Predicting metastasis at initial diagnosis and radiotherapy effectiveness in patients with metastatic osteosarcoma. J Cancer Res Clin Oncol 2023; 149:9587-9595. [PMID: 37222812 PMCID: PMC10423143 DOI: 10.1007/s00432-023-04869-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: 04/09/2023] [Accepted: 05/19/2023] [Indexed: 05/25/2023]
Abstract
Osteosarcoma is a primary malignant bone tumor affecting mostly children and adolescents. The overall 10 year survivals of patients with metastatic osteosarcoma are typically less than 20% in the literature and remain concerning. We aimed to develop a nomogram for predicting the risk of metastasis at initial diagnosis in patients with osteosarcoma and evaluate the effectiveness of radiotherapy in patients with metastatic osteosarcoma. Clinical and demographic data of patients with osteosarcoma were collected from the surveillance, epidemiology, and end results database. We randomly split our analytical sample into the training and validation cohorts, then established and validated a nomogram for predicting the risk of osteosarcoma metastasis at initial diagnosis. The effectiveness of radiotherapy was evaluated by performing propensity score matching in patients underwent surgery + chemotherapy and those underwent surgery + chemotherapy + radiotherapy, among patients with metastatic osteosarcoma. 1439 patients met the inclusion criteria and were included in this study. 343 of 1439 had osteosarcoma metastasis by the time of initial presentation. A nomogram for predicting the likelihood of osteosarcoma metastasis by the time of initial presentation was developed. In both unmatched and matched samples, the radiotherapy group demonstrated a superior survival profile comparing with the non-radiotherapy group. Our study established a novel nomogram to evaluate the risk of osteosarcoma with metastasis, and demonstrated that radiotherapy combined with chemotherapy and surgical resection could improve 10-year survival in patients with metastasis. These findings may guide the clinical decision-making for orthopedic surgeons.
Collapse
Affiliation(s)
- Wenhao Chen
- Department of Orthopedic Surgery, National Children's Regional Medical Center, National Clinical Research Center for Child Health, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, Zhejiang, China.
| | - Xinyu He
- Department of Child Health Care, National Children's Regional Medical Center, National Clinical Research Center for Child Health, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, Zhejiang, China
| | - Zhiyu Yan
- Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Xiuquan Lin
- Department for Chronic and Non-Communicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, 386 Chong'an Road, Fuzhou, 350012, Fujian, China.
- The School of Public Health, Fujian Medical University, 1 North Xuefu Road, Fuzhou, 350122, Fujian, China.
| | - Guannan Bai
- Department of Child Health Care, National Children's Regional Medical Center, National Clinical Research Center for Child Health, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, Zhejiang, China.
| |
Collapse
|
12
|
Zhanghuang C, Wang J, Zhang Z, Yao Z, Ji F, Li L, Xie Y, Yang Z, Tang H, Zhang K, Wu C, Yan B. A nomogram for predicting cancer-specific survival and overall survival in elderly patients with nonmetastatic renal cell carcinoma. Front Surg 2023; 9:1018579. [PMID: 36684269 PMCID: PMC9852727 DOI: 10.3389/fsurg.2022.1018579] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 11/28/2022] [Indexed: 01/08/2023] Open
Abstract
Background Renal cell carcinoma (RCC) is a common malignant tumor in the elderly, with an increasing trend in recent years. We aimed to construct a nomogram of cancer-specific survival (CSS) and overall survival (OS) in elderly patients with nonmetastatic renal cell carcinoma (nmRCC). Methods Clinicopathological information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program in elderly patients with nmRCC from 2010 to 2015. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression analyses were used to identify independent risk factors for patient outcomes in the training cohort. A nomogram was constructed based on these independent risk factors to predict the 1-, 3-, and 5-year CSS and OS in elderly patients with nmRCC. We used a range of methods to validate the accuracy and reliability of the model, including the calibration curve, consistency index (C-index), and the area under the receiver operating curve (AUC). Decision curve analysis (DCA) was used to test the clinical utility of the model. Results A total of 12,116 patients were enrolled in the study. Patients were randomly assigned to the training cohort (N = 8,514) and validation cohort (N = 3,602). In the training cohort, univariate and multivariate Cox regression analysis showed that age, marriage, tumor histological type, histological tumor grade, TN stage, tumor size, and surgery are independent risk factors for prognosis. A nomogram was constructed based on independent risk factors to predict CSS and OS at 1-, 3-, and 5- years in elderly patients with nmRCC. The C-index of the training and validation cohorts in CSS were 0.826 and 0.831; in OS, they were 0.733 and 0.734, respectively. The AUC results of the training and validation cohort were similar to the C-index. The calibration curve indicated that the observed value is highly consistent with the predicted value, meaning the model has good accuracy. DCA results suggest that the clinical significance of the nomogram is better than that of traditional TNM staging. Conclusions We built a nomogram prediction model to predict the 1-, 3- and 5-year CSS and OS of elderly nmRCC patients. This model has good accuracy and discrimination and can help doctors and patients make clinical decisions and active monitoring.
Collapse
Affiliation(s)
- Chenghao Zhanghuang
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China,Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China,Yunnan Key Laboratory of Children’s Major Disease Research, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Kunming, China
| | - Jinkui Wang
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaoxia Zhang
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Zhigang Yao
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China
| | - Fengming Ji
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China
| | - Li Li
- Yunnan Key Laboratory of Children’s Major Disease Research, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Kunming, China
| | - Yucheng Xie
- Department of Pathology, Kunming Children's Hospital, Children’s Hospital Affiliated to Kunming Medical University, Kunming, China
| | - Zhen Yang
- Department of Oncology, Yunnan Children Solid Tumor Treatment Center, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Kunming, China
| | - Haoyu Tang
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China
| | - Kun Zhang
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China
| | - Chengchuang Wu
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China
| | - Bing Yan
- Department of Urology, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Yunnan Province Clinical Research Center for Children’s Health and Disease, Kunming, China,Yunnan Key Laboratory of Children’s Major Disease Research, Kunming Children’s Hospital, Children’s Hospital Affiliated to Kunming Medical University, Kunming, China,Correspondence: Bing Yan
| |
Collapse
|
13
|
Wen C, Tang J, Wang T, Luo H. A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer. BMC Gastroenterol 2022; 22:444. [PMID: 36324087 PMCID: PMC9632126 DOI: 10.1186/s12876-022-02544-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2022] Open
Abstract
Background Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients. Method We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram. Result A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system. Conclusion We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-022-02544-y.
Collapse
Affiliation(s)
- Chong Wen
- General Surgery Center, The General Hospital of Western Theater, Chengdu, 610083, Sichuan Province, China.,College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Jie Tang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - Tao Wang
- General Surgery Center, The General Hospital of Western Theater, Chengdu, 610083, Sichuan Province, China.
| | - Hao Luo
- General Surgery Center, The General Hospital of Western Theater, Chengdu, 610083, Sichuan Province, China.
| |
Collapse
|