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Qiu P, Guo Q, Pan K, Lin J. Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma. BMC Cancer 2024; 24:235. [PMID: 38378515 PMCID: PMC10877775 DOI: 10.1186/s12885-024-12004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.
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Affiliation(s)
- Pengjun Qiu
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qiaonan Guo
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Kelun Pan
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Jianqing Lin
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
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Huang F, Lyu GR, Lai QQ, Li YZ. Nomogram model for predicting invasive placenta in patients with placenta previa: integrating MRI findings and clinical characteristics. Sci Rep 2024; 14:200. [PMID: 38167630 PMCID: PMC10761737 DOI: 10.1038/s41598-023-50900-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: 02/18/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
This study aims to validate a nomogram model that predicts invasive placenta in patients with placenta previa, utilizing MRI findings and clinical characteristics. A retrospective analysis was conducted on a training cohort of 269 patients from the Second Affiliated Hospital of Fujian Medical University and a validation cohort of 41 patients from Quanzhou Children's Hospital. Patients were classified into noninvasive and invasive placenta groups based on pathological reports and intraoperative findings. Three clinical characteristics and eight MRI signs were collected and analyzed to identify risk factors and develop the nomogram model. The mode's performance was evaluated in terms of its discrimination, calibration, and clinical utility. Independent risk factors incorporated into the nomogram included the number of previous cesarean sections ≥ 2 (odds ratio [OR] 3.32; 95% confidence interval [CI] 1.28-8.59), type-II placental bulge (OR 17.54; 95% CI 3.53-87.17), placenta covering the scar (OR 2.92; CI 1.23-6.96), and placental protrusion sign (OR 4.01; CI 1.06-15.18). The area under the curve (AUC) was 0.908 for the training cohort and 0.803 for external validation. The study successfully developed a highly accurate nomogram model for predicting invasive placenta in placenta previa cases, based on MRI signs and clinical characteristics.
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Affiliation(s)
- Fang Huang
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Department of Radiology, The Second Affiliated Clinical Medical College of Fujian Medical University, Quanzhou, China
| | - Guo-Rong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
- Department of Ultrasound, The Second Affiliated Clinical Medical College of Fujian Medical University, Quanzhou, China.
| | - Qing-Quan Lai
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Department of Radiology, The Second Affiliated Clinical Medical College of Fujian Medical University, Quanzhou, China
| | - Yuan-Zhe Li
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Department of Radiology, The Second Affiliated Clinical Medical College of Fujian Medical University, Quanzhou, China
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Wang S, Wu W, Lin X, Zhang KM, Wu Q, Luo M, Zhou J. Predictive and prognostic biomarkers of bone metastasis in breast cancer: current status and future directions. Cell Biosci 2023; 13:224. [PMID: 38041134 PMCID: PMC10693103 DOI: 10.1186/s13578-023-01171-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/10/2023] [Indexed: 12/03/2023] Open
Abstract
The most common site of metastasis in breast cancer is the bone, where the balance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation is disrupted. This imbalance causes osteolytic bone metastasis in breast cancer, which leads to bone pain, pathological fractures, spinal cord compression, and other skeletal-related events (SREs). These complications reduce patients' quality of life significantly and have a profound impact on prognosis. In this review, we begin by providing a brief overview of the epidemiology of bone metastasis in breast cancer, including current diagnostic tools, treatment approaches, and existing challenges. Then, we will introduce the pathophysiology of breast cancer bone metastasis (BCBM) and the animal models involved in the study of BCBM. We then come to the focus of this paper: a discussion of several biomarkers that have the potential to provide predictive and prognostic value in the context of BCBM-some of which may be particularly compatible with more comprehensive liquid biopsies. Beyond that, we briefly explore the potential of new technologies such as single-cell sequencing and organoid models, which will improve our understanding of tumor heterogeneity and aid in the development of improved biomarkers. The emerging biomarkers discussed hold promise for future clinical application, aiding in the prevention of BCBM, improving the prognosis of patients, and guiding the implementation of personalized medicine.
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Affiliation(s)
- Shenkangle Wang
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, China
| | - Wenxin Wu
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, China
| | - Xixi Lin
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, China
| | | | - QingLiang Wu
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
- Hangzhou Ninth People's Hospital, Hangzhou, 310014, China
| | - Mingpeng Luo
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China.
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, China.
- The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310014, China.
| | - Jichun Zhou
- Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China.
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Hangzhou, China.
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Gao B, Ou XL, Li MF, Wang MD, Huang F. Risk stratification system and visualized dynamic nomogram constructed for predicting diagnosis and prognosis in rare male breast cancer patients with bone metastases. Front Endocrinol (Lausanne) 2022; 13:1013338. [PMID: 36440188 PMCID: PMC9691876 DOI: 10.3389/fendo.2022.1013338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background Bone metastases (BM) from malignant tumors could disrupt the balance between osteoclasts and osteoblasts and affect bone homeostasis. Malignant breast cancer (BC) is rare in male patients, and co-occurrence of BM is even rarer. Given its low incidence, there is limited research evaluating risk and prognosis. Despite the widespread application of nomograms to predict uncommon malignancies, no studies have constructed predictive models focusing on the diagnosis and prognosis of male breast cancer with bone metastases (MBCBM). Methods This study selected all male breast cancer patients (MBC) between 2010 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database. We used simple and multivariate Logistic regression analyses to identify independent risk factors for BM in MBC patients. Then simple and multivariate Cox regression analyses were employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in MBCBM patients. We established and validated three new nomograms based on these independent factors. Result A total of 4187 MBC patients were included, with 191 (4.56%) having bone metastases at the time of diagnosis. The independent risk factors of BM in MBC patients included age, tumor size, marital status, T stage, and N stage. In MBCBM patients, independent prognostic factors for OS and CSS were both age, T stage, ER status, PR status, and surgery. The concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve analysis (DCA) confirmed that these three nomograms could accurately predict the diagnosis and prognosis of MBCBM patients with excellent discrimination and clinical utility superior to the TNM staging system. We then established two prognostic-based risk stratification systems and three visualized dynamic nomograms that could be applied in clinical practice. Conclusion In conclusion, this study aimed to establish and validate an accurate novel nomogram to objectively predict the diagnosis and prognosis of MBCBM patients. On this basis, prognostic-based risk stratification systems and visualized dynamic nomograms were constructed to facilitate doctors and patients to quantify individual BM risk probability and survival probability to assist in personalized risk assessment and clinical decision-making.
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Affiliation(s)
- Bing Gao
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xiao-lan Ou
- Department of Hand Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Mu-feng Li
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, China
| | - Meng-die Wang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fei Huang
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, China
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The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities—A Comparison of Prognostic Models. Curr Oncol 2022; 29:4703-4716. [PMID: 35877233 PMCID: PMC9320475 DOI: 10.3390/curroncol29070373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/19/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022] Open
Abstract
Individualized survival prognostic models for symptomatic patients with appendicular metastatic bone disease are key to guiding clinical decision-making for the orthopedic surgeon. Several prognostic models have been developed in recent years; however, most orthopedic surgeons have not incorporated these models into routine practice. This is possibly due to uncertainty concerning their accuracy and the lack of comparison publications and recommendations. Our aim was to conduct a review and quality assessment of these models. A computerized literature search in MEDLINE, EMBASE and PubMed up to February 2022 was done, using keywords: “Bone metastasis”, “survival”, “extremity” and “prognosis”. We evaluated each model’s performance, assessing the estimated discriminative power and calibration accuracy for the analyzed patients. We included 11 studies out of the 1779 citations initially retrieved. The 11 studies included seven different models for estimating survival. Among externally validated survival prediction scores, PATHFx 3.0, 2013-SPRING and potentially Optimodel were found to be the best models in terms of performance. Currently, it is still a challenge to recommend any of the models as the standard for predicting survival for these patients. However, some models show better performance status and other quality characteristics. We recommend future, large, multicenter, prospective studies to compare between PATHfx 3.0, SPRING 2013 and OptiModel using the same external validation dataset.
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Li G, Zhang D. Development and Validation of Prognostic Nomogram for Elderly Breast Cancer: A Large-Cohort Retrospective Study. Int J Gen Med 2022; 15:87-101. [PMID: 35018116 PMCID: PMC8742678 DOI: 10.2147/ijgm.s343850] [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: 10/28/2021] [Accepted: 12/16/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Our research aims to study the bone metastatic patterns and prognostic outcomes in elderly breast cancer (BC) and to develop elder-specific nomograms. Methods We downloaded the data of BC patients between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database. The differences in clinical features and prognosis between young (age < 65) and elderly (age ≥ 65) BC patients were compared. The univariate and multivariate Cox analyses were used to determine the overall survival (OS)- and cancer-specific survival (CSS)-related variables and establish two nomograms of BC patients with bone metastasis (BCBM). The receiver operating characteristic (ROC) curve with area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival curve were selected to evaluate nomograms. Results A total of 230,177 BC patients were enrolled in our research, including 142,025 young and 88,152 elderly patients. The prognosis of elderly BCBM patients was significantly worse than young patients. Age, race, breast subtype, tumor size, tumor grade, brain metastasis, liver metastasis, surgery, and chemotherapy were independent prognostic variables for elderly BCBM patients, including OS and CSS. The AUC values at 12, 18, and 24 months were 0.750, 0.751, and 0.739 for OS nomogram and 0.759, 0.762, and 0.752 for CSS nomogram in the training cohort, which were higher than the AUC values of all single independent prognostic variables. The survival curve showed a distinct prognosis between low-, median- and high-risk groups (p < 0.001). Finally, calibration curves and DCA indicated that both nomograms have favorable performance. Conclusion Elderly and young patients presented with different bone metastatic frequencies, clinical features, and prognostic outcomes. Two elder-specific nomograms incorporating nine clinical variables were established and validated to be a valuable predictor for elderly BCBM patients.
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Affiliation(s)
- Gangfeng Li
- Clinical Laboratory Center of Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medcine), Shaoxing, Zhejiang, 312000, People's Republic of China
| | - Dan Zhang
- Clinical Laboratory Center of Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medcine), Shaoxing, Zhejiang, 312000, People's Republic of China
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