Further information on dose of minocycline, black thyroid and metastatic lymph node from papillary thyroid cancer.
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ABSTRACT:
SUBMITTER: Kim MJ
PROVIDER: S-EPMC10333770 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
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Further information on dose of minocycline, black thyroid and metastatic lymph node from papillary thyroid cancer.
Gland surgery 20230529 6
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| S-EPMC10681174 | biostudies-literature
Project description:BackgroundThe objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).MethodsA retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.ResultsAmong the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.ConclusionThe predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.
| S-EPMC11497514 | biostudies-literature
Project description:Lin28 is involved in the progression of several types of tumors. Data collected from clinical trials have suggested that Lin28 expression is correlated with poor prognosis in thyroid carcinoma. The present study was conducted to investigate the association between Lin28 expression and the clinicopathological parameters of papillary thyroid carcinoma (PTC). Accordingly, the clinical data and diagnostic results from 237 patients with PTC were collected. Immunohistochemical staining was performed to evaluate the Lin28 expression levels in thyroid tissue samples. Associations between the expression levels and clinicopathological parameters were evaluated. Lin28 was expressed in 96/237 (40.5%) of PTC specimens. Compared with patients with no Lin28 expression, patients with expression had higher rates of lymph node metastasis (P<0.001) and larger tumors (P=0.011). Multivariate analysis revealed that Lin28 was associated with lymph node metastasis. Next, bioinformatics analysis was performed based using the Gene Expression Omnibus database and The Cancer Genome Atlas database. Lin28 expression was associated with aggressive tumor characteristics, such as lymph node metastasis and larger tumors. In conclusion, the present study revealed that Lin28 expression served as a risk factor for lymph node metastasis. Accordingly, Lin28 expression may be used as a prognostic marker to predict lymph node metastasis in patients with PTC. In addition, Lin28 may serve as a therapeutic target in the management of this tumor type, which may help improve patient outcomes.
| S-EPMC7751344 | biostudies-literature
Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.
| S-EPMC10733258 | biostudies-literature
Project description:Medullary thyroid cancer (MTC) has a propensity to cervical lymph node metastases (LNM). Recent studies have shown that both the number of involved lymph nodes (LNs) and the metastatic lymph node ratio (MLNR) confer prognostic information. This study was to determine the predictive value of MLNR on cancer-specific survival (CSS) in SEER (Surveillance, Epidemiology and End Results)-registered MTC patients treated with thyroidectomy and lymphadenectomy between 1991 and 2012, investigate the cutoff points for MLNR in stratifying risk of mortality and provide evidence for selection of appropriate treatment strategies. X-tile program determined 0.5 as optimal cut-off value for MLNR in terms of CSS in 890 MTC patients. According to multivariate Cox regression analysis, MLNR (0.50-1.00) is a significant independent prognostic factor for CSS (hazard ratio 2.161, 95% confidence interval 1.327-3.519, p=0.002). MLNR (0.50-1.00) has a greater prognostic impact on CSS in female, non-Hispanic white, T3/4, N1b and M1 patients. The lymph node yield (LNY) influences the effect of MLNR on CSS; LNY ≥9 results in MLNR (0.50-1.00) having a higher HR for CSS than MLNR (0.00-0.49). In conclusion, higher MLNRs predict poorer survival in MTC patients. Eradication of involved nodes ensures accurate staging and maximizes the ability of MLNR to predict prognosis.
| S-EPMC5323204 | biostudies-literature
Project description:BackgroundThe sentinel lymph node (SLN) is defined as the first draining node from the primary lesion, and it has proven to be a good indicator of the metastatic status of regional lymph nodes in solid tumors. The aim of this study was to evaluate the clinical application of SLN biopsy (SLNB) in papillary thyroid carcinoma (PTC) with occult lymph nodes.MethodsFrom April 2006 to October 2012, 212 consecutive PTC patients were treated with SLNB using carbon nanoparticle suspension (CNS). Then, the stained nodes defined as SLN were collected, and prophylactic central compartment neck dissection (CCND) followed by total thyroidectomy or subtotal thyroidectomy were performed. All the samples were sent for pathological examination.ResultsThere were 78 (36.8%) SLN metastasis (SLNM)-positive cases and 134 (63.2%) SLNM-negative cases. The sensitivity, specificity, positive and negative predictive values, and false-positive and false-negative rates of SLNB were 78.8%, 100%, 100%, 84.3%, 0%, and 21.2%, respectively. The PTC patients with SLNM were more likely to be male (48.2% vs. 32.7%, p = 0.039) and exhibited multifocality (52.6% vs. 33.3%, p = 0.025) and extrathyroidal extension (56.7% vs. 33.5%, p = 0.015). A greater incidence of non-SLN metastases in the central compartment was found in patients with SLNM (41/78, 52.6%) than in those without SLNM (21/134, 15.7%; p < 0.05). However, the SLNM-negative PTC patients with non-SLN metastases were more likely to be male (37.9% vs. 9.5%, p < 0.05).ConclusionsThe application of SLNB using CNS is technically feasible, safe, and useful, especially for male patients with co-existing multifocality and extrathyroidal extension. However, the sensitivity of SLNB must be improved and its false-negative rate reduced before it can be a routine procedure and replace prophylactic CCND. More attention should be paid to PTC patients (especially males) without SLNM for signs of non-SLN metastases.
| S-EPMC4457868 | biostudies-literature
Project description:BackgroundThyroglobulin measurement with fine-needle aspiration (Tg-FNA) is a sensitive method for detecting metastatic papillary thyroid carcinoma (PTC). However, the diagnostic threshold is not well established and the influence of the thyroid gland on the cutoff value is also controversial. In this study, patients were classified into two groups according to the presence or absence of thyroid tissue, to determine an appropriate cutoff value for clinical practice.MethodsPatients with a history of thyroid nodules or surgery for PTC and with enlarged cervical lymph nodes on an FNA examination were enrolled for Tg-FNA detection.ResultsOne hundred ninety-six lymph nodes (189 patients) were included: 100 from preoperative patients, 49 from patients treated with partial thyroid ablation, and 47 from patients with total thyroid ablation. In 149 lymph nodes from patient with thyroids, the cutoff value for Tg-FNA was 55.99 ng/mL (sensitivity, 95.1%; specificity, 100%), whereas in 47 lymph nodes from patients without a thyroid, it was 9.71 ng/mL (sensitivity, 96.7%; specificity, 100%). Thus, the cutoff value for Tg-FNA was higher in patients with thyroids than in patients without thyroids.ConclusionsThe cutoff value for Tg-FNA is influenced by residual thyroid tissue, and a higher cutoff value is recommended for patients with thyroids than for patients without thyroids.
| S-EPMC5410021 | biostudies-literature
Project description:BackgroundWhether prophylactic central lymph node dissection is necessary for patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC) remains controversial. Herein, we aimed to establish an ultrasound (US) radiomics (Rad) score for assessing the probability of central lymph node metastasis (CLNM) in such patients.Methods480 patients (327 in the training cohort, 153 in the validation cohort) who underwent thyroid surgery for cN0 PTMC at two institutions between January 2018 and December 2020 were included. Radiomics features were extracted from the US images. Least absolute shrinkage and selection operator logistic regression were utilized to generate a Rad score. A nomogram consisting of the Rad score and clinical factors was then constructed for the training cohort. Both cohorts assessed model performance using discrimination, calibration, and clinical usefulness.ResultsBased on the six most valuable radiomics features, the Rad score was calculated for each patient. A multivariate analysis revealed that a higher Rad score (P < 0.001), younger age (P = 0.006), and presence of capsule invasion (P = 0.030) were independently associated with CLNM. A nomogram integrating these three factors demonstrated good calibration and promising clinical utility in the training and validation cohorts. The nomogram yielded areas under the curve of 0.795 (95% confidence interval [CI], 0.745-0.846) and 0.774 (95% CI, 0.696-0.852) in the training and validation cohorts, respectively.ConclusionsThe radiomics nomogram may be a clinically useful tool for the individual prediction of CLNM in patients with cN0 PTMC.
| S-EPMC10875890 | biostudies-literature