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Frontiers in Oncology 2024Extraocular sebaceous carcinoma (SC), particularly those outside the head and neck region, is rare and not well-described.
INTRODUCTION
Extraocular sebaceous carcinoma (SC), particularly those outside the head and neck region, is rare and not well-described.
PURPOSE
This study aimed to explore the epidemiology and identify the prognostic factors of non-head and neck SC, describe the possible relevant factors of distant metastasis, and provide implications for distant metastasis screening.
METHODS
Data from the 17 registries in the Surveillance, Epidemiology, and End Results database were retrospectively collected for patients with SC outside the head and neck from 2000 through 2020. Overall survival (OS) and disease-specific survival (DSS) were the primary endpoints. Survival analysis was conducted through Kaplan-Meier curves, and multivariate analysis was carried out using Cox proportional hazard models.
RESULTS
A total of 1,237 patients with SC outside the head and neck were identified. The mean age at diagnosis of the entire patient cohort was 67.7 years (30 to 90+ years), and the mean tumor size was 2.2 cm (0.1-16 cm). Patients with distant disease experienced the lowest OS (mean, 29.5 months) than those with localized disease and regional disease ( < 0.0001). Multivariate analysis revealed that age, tumor size, and stage were independent determinants of OS; age, stage, and primary site were independent determinants of DSS. Tumor grade and lymph node status had less prognostic value for survival. Undifferentiated tumors have a trend toward distant metastasis, especially those at the primary site of the trunk.
CONCLUSION
The prognosis of the non-head and neck SC is excellent, while the survival of distant disease is very poor. Distant metastasis screening can be considered for undifferentiated tumors, especially those located in the trunk region with large tumor sizes.
PubMed: 38800410
DOI: 10.3389/fonc.2024.1395273 -
European Journal of Medical Research Dec 2022Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC.
METHODS
A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.
RESULTS
Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001.
CONCLUSIONS
We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
Topics: Humans; Esophageal Neoplasms; Algorithms; Tomography, X-Ray Computed; Ethnicity; Multivariate Analysis
PubMed: 36463269
DOI: 10.1186/s40001-022-00877-8 -
BMC Cancer Mar 2023Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the...
BACKGROUND
Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.
METHODS
We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.
RESULTS
Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.
CONCLUSION
Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.
Topics: Humans; Female; Breast Neoplasms; Retrospective Studies; Artificial Intelligence; Nomograms; Bone Neoplasms
PubMed: 36918809
DOI: 10.1186/s12885-023-10704-w -
Endocrine Jun 2024Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk...
OBJECTIVE
Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of thyroid cancer through the construction of machine learning models to provide a reference for clinical diagnosis and treatment.
MATERIALS & METHODS
Data on demographic and clinicopathological characteristics of thyroid cancer patients between 2010 and 2015 were extracted from the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database. Our research used univariate and multivariate logistic models to screen independent risk factors, respectively. Decision Trees (DT), ElasticNet (ENET), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function Support Vector Machine (RBFSVM) and seven machine learning models were compared and evaluated by the following metrics: the area under receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity(also called recall), specificity, precision, accuracy and F1 score. Interpretable machine learning was used to identify possible correlation between variables and distant metastasis.
RESULTS
Independent risk factors for distant metastasis, including age, gender, race, marital status, histological type, capsular invasion, and number of lymph nodes metastases were screened by multifactorial regression analysis. Among the seven machine learning algorithms, RF was the best algorithm, with an AUC of 0.948, sensitivity of 0.919, accuracy of 0.845, and F1 score of 0.886 in the training set, and an AUC of 0.960, sensitivity of 0.929, accuracy of 0.906, and F1 score of 0.908 in the test set.
CONCLUSIONS
The machine learning model constructed in this study helps in the early diagnosis of distant thyroid metastases and helps physicians to make better decisions and medical interventions.
Topics: Humans; Thyroid Neoplasms; Female; Machine Learning; Male; SEER Program; Middle Aged; Adult; Aged; Risk Factors; Prognosis; Neoplasm Metastasis; Databases, Factual
PubMed: 38155324
DOI: 10.1007/s12020-023-03657-4 -
Frontiers in Oncology 2022Breast cancer is one of the most commonly diagnosed cancers, and the fourth leading cause of cancer deaths in females worldwide. Sarcopenia is related to adverse...
BACKGROUND
Breast cancer is one of the most commonly diagnosed cancers, and the fourth leading cause of cancer deaths in females worldwide. Sarcopenia is related to adverse clinical outcomes in patients with malignancies. Muscle index is a key parameter in evaluating sarcopenia. However, there is no data investigating the association between muscle index and distant metastasis in breast cancer. The aim of this study was to explore whether muscle index can effectively predict distant metastasis and death outcomes in breast cancer patients.
STUDY DESIGN
The clinical data of 493 breast cancer patients at the Harbin Medical University Cancer Hospital between January 2014 and December 2015 were retrospectively analyzed. Quantitative measurements of pectoralis muscle area and skeletal muscle area were performed at the level of the fourth thoracic vertebra (T4) and the eleventh thoracic vertebra (T11) of the chest computed tomography image, respectively. The pectoralis muscle index (PMI) and skeletal muscle index (SMI) were assessed by the normalized muscle area (area/the square of height). Survival analysis was performed using the log-rank test and Cox proportional hazards regression analysis.
RESULT
The patients with metastases had lower PMI at T4 level (PMI/T4) and SMI at T11 level (SMI/T11) compared with the patients without metastases. Moreover, there were significant correlations between PMI/T4 and lymphovascular invasion, Ki67 expression, multifocal disease, and molecular subtype. In addition, multivariate analysis revealed that PMI/T4, not SMI/T11, was an independent prognostic factor for distant metastasis-free survival (DMFS) and overall survival (OS) in breast cancer patients.
CONCLUSIONS
Low PMI/T4 is associated with worse DMFS and OS in breast cancer patients. Future prospective studies are needed.
PubMed: 35574329
DOI: 10.3389/fonc.2022.854137 -
Cancer Management and Research 2021Mucinous adenocarcinoma (MA) is a subtype of colorectal cancer (CRC) associated with a higher incidence of local extension and worse survival compared to non-mucinous...
BACKGROUND
Mucinous adenocarcinoma (MA) is a subtype of colorectal cancer (CRC) associated with a higher incidence of local extension and worse survival compared to non-mucinous adenocarcinoma, but few studies have investigated surgery-related predictors for recurrence of MA. Therefore, we aimed to elucidate the predictors for local recurrence and remote metastasis of MA after surgery.
PATIENTS AND METHODS
This study retrospectively analyzed 162 patients with mucinous colorectal adenocarcinoma (MAC) after radical resection. Analysis variables included demographics, clinical indicators, pathologic stage, surgical procedure, adjuvant therapy, and recurrence. Univariate and multivariate analyses were performed to investigate the risk factors for local and distant tumor relapse.
RESULTS
A total of 162 patients (86 male) with a mean age of 58.26 years were included; 70.37% of patients had colonic tumors, and 29.63% had rectal tumors. The 5-year disease-free survival (DFS) rates for these patients were as follows: 100% for TNM stage I, 71.2% for stage II, and 47.8% for stage III. Five-year DFS rates of MAC, colonic and rectal MA were 62.0%, 65.8%, and 51.7%, respectively. Local recurrence occurred in 38 patients and distant metastasis in 33 patients. In univariate analysis, predictors for local recurrence of MAC were intraoperative blood loss, intraoperative transfusion, and N2 stage; and predictors for distant metastasis were male sex, CA199, CEA, intraoperative blood loss, T4 stage, and N2 stage. In multivariate analysis, predictors for local recurrence of MAC were intraoperative transfusion (=0.04, =4.175) and N2 stage (=0.000, =5.291), and predictors for distant metastasis were male sex (=0.049, =2.410), CA199 (=0.02, =1.003), and T4 stage (=0.007, =4.006).
CONCLUSION
Intraoperative transfusion and N2 stage were significant predictors for local recurrence. Male sex, CA199, and T4 stage were significant predictors for distant metastasis. Knowledge of the risk factors for postoperative recurrence provides a basis for logical approaches to treatment and follow-up of MAC.
PubMed: 34168497
DOI: 10.2147/CMAR.S313627 -
BMC Urology Dec 2022Urothelial carcinoma is the most common type of bladder cancer worldwide and it has a poor prognosis for patients with distant metastasis. Nomograms are frequently used...
Constructing and validating nomograms to predict risk and prognostic factors of distant metastasis in urothelial bladder cancer patients: a population-based retrospective study.
BACKGROUND
Urothelial carcinoma is the most common type of bladder cancer worldwide and it has a poor prognosis for patients with distant metastasis. Nomograms are frequently used in clinical research, but no research has evaluated the diagnostic and prognostic factors of distant metastasis in urothelial bladder cancer (UBC).
METHODS
The Surveillance, Epidemiology, and End Results database was used to analyze all patients diagnosed with UBC between 2000 and 2017. Lasso regression was used to identify the potential risk predictive factors for distant metastasis in UBC. Univariate and multivariate Cox proportional hazard regression analyses were performed to determine independent prognostic factors for distant metastasis urothelial bladder cancer (DMUBC). Subsequently, two nomograms were constructed based on the above models. The receiver operating characteristic (ROC), and calibration curves were performed to evaluate the two nomograms.
RESULTS
The study included 73,264 patients with UBC, with 2,129 (2.9%) having distant metastasis at the time of diagnosis. In the diagnostic model, tumor size, histologic type, and stage N and T were all important risk predictive factors for distant metastasis of UBC. In the prognostic model, age, tumor size, surgery, and chemotherapy were independent factors affecting the prognosis of DMUBC. DCA, ROC, calibration, and Kaplan-Meier (K-M) survival curves reveal that the two nomograms can effectively predict the diagnosis and prognosis of DMUBC.
CONCLUSION
The developed nomograms are practical methods for predicting the occurrence risk and prognosis of distant metastasis urothelial bladder cancer patients, which may benefit the clinical decision-making process.
Topics: Humans; Carcinoma, Transitional Cell; Urinary Bladder Neoplasms; Nomograms; Retrospective Studies; Prognosis; Risk Factors; Neoplasm Staging
PubMed: 36575440
DOI: 10.1186/s12894-022-01166-6 -
IEEE Journal of Biomedical and Health... Oct 2021Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art...
Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.
Topics: COVID-19; Humans; Machine Learning; Tomography, X-Ray Computed
PubMed: 33449887
DOI: 10.1109/JBHI.2021.3051470 -
Gynecologic Oncology Jun 2022To determine the effect of distance to closest negative margin on survival after pelvic exenteration (PE).
OBJECTIVE
To determine the effect of distance to closest negative margin on survival after pelvic exenteration (PE).
METHODS
In this retrospective analysis of PE at Moffitt Cancer Center from 2000 to 2019, baseline characteristics, clinical details, and outcomes were ascertained. Distance to closest negative margin was measured. Close and distant negative margins were defined as <3 mm and ≥3 mm from malignancy to nearest surgical margin, respectively. Overall survival (OS) and progression-free survival (PFS) were determined, and Kaplan-Meier curves were compared. Cox proportional hazards regression was used to examine the association of margin status with OS and PFS.
RESULTS
Of 124 PEs with malignancy, 80 (64.5%) had negative margins. Median survival was 62 (95% confidence interval [CI] 27-70) months for negative and 21 (95% CI 15-29) months for positive margins. Of 76 with negative margins and documented margin length, 26 had close and 50 had distant margins. Median survival was 32 (95% CI 14-62) months for close and 111 (95% CI 42-166) months for distant margins. Distant margins were associated with improved OS (p = 0.0054) and PFS (p = 0.0099) compared to close margins. After adjusting for other prognostic factors, patients with distant margins had significantly decreased risk of all-cause mortality (HR 0.39, 95% CI 0.19-0.78; p = 0.008) and progression (HR 0.48, 95% CI 0.23-0.99; p = 0.04) compared to positive margins. No significant differences in OS or PFS were observed between close and positive margins. This survival benefit remained among those with cervical cancer. Median survival in this cohort was 34.1 (95% CI 2.0-69.8) months for close and 165.7 (95% CI 24.5-165.7) for distant margins.
CONCLUSIONS
Distant margins following PE are associated with improved OS and PFS compared to close margins.
Topics: Female; Humans; Margins of Excision; Neoplasm Recurrence, Local; Pelvic Exenteration; Progression-Free Survival; Retrospective Studies; Uterine Cervical Neoplasms
PubMed: 35487774
DOI: 10.1016/j.ygyno.2022.04.004 -
Cancer Epidemiology, Biomarkers &... Jun 2022This study examines the association between Medicaid enrollment, including through the National Breast and Cervical Cancer Early Detection Program (NBCCEDP), and distant...
BACKGROUND
This study examines the association between Medicaid enrollment, including through the National Breast and Cervical Cancer Early Detection Program (NBCCEDP), and distant stage for three screening-amenable cancers: breast, cervical, and colorectal.
METHODS
We use the Surveillance, Epidemiology, and End Results Cancer Registry linked with Medicaid enrollment data to compare patients who were Medicaid insured with patients who were not Medicaid insured. We estimate the likelihood of distant stage at diagnosis using logistic regression.
RESULTS
Medicaid enrollment following diagnosis was associated with the highest likelihood of distant stage. Medicaid enrollment through NBCCEDP did not mitigate the likelihood of distant stage disease relative to Medicaid enrollment prior to diagnosis. Non-Hispanic Black patients had a greater likelihood of distant stage breast and colorectal cancer. Residing in higher socioeconomic areas was associated with a lower likelihood of distant stage breast cancer.
CONCLUSIONS
Medicaid enrollment prior to diagnosis is associated with a lower likelihood of distant stage in screen amenable cancers but does not fully ameliorate disparities.
IMPACT
Our study highlights the importance of health insurance coverage prior to diagnosis and demonstrates that while targeted programs such as the NBCCEDP provide critical access to screening, they are not a substitute for comprehensive insurance coverage.
Topics: Breast Neoplasms; Early Detection of Cancer; Female; Humans; Insurance Coverage; Mass Screening; Medicaid; Neoplasm Staging; United States
PubMed: 35322273
DOI: 10.1158/1055-9965.EPI-21-1077