An Effective Ultrasound Features-Based Diagnostic Model via Principal Component Analysis Facilitated Differentiating Subtypes of Mucinous Breast Cancer From Fibroadenomas.

BACKGROUND: Mucinous breast carcinoma (MBC) is often misdiagnosed as fibroadenoma (FA),which can lead to inappropriate or delayed treatments. This study aimed to establish an efficient ultrasound (US)-based diagnostic model to distinguish MBC subtypes from FAs. METHODS: Between January 2017 and February 2024, 240 lesions were enrolled, comprising 65 cases of pure mucinous breast carcinoma (PMBC), 47 cases of mixed mucinous breast carcinoma (MMBC), and 128 cases of FAs. Ten US feature variables underwent principal component analysis (PCA). Models were constructed based on components explaining over 75% of the total variation, with varimax rotation applied for interpretability. Comprehensive models were developed to distinguish PMBCs and MMBCs from FAs. RESULTS: Six principal components were selected, achieving a cumulative contribution rate of 77.46% for PMBCs vs. FAs and 78.62% for MMBCs vs. FAs. The principal component of cystic-solid composition and posterior acoustic enhancement demonstrated the highest diagnostic value for distinguishing PMBCs from FAs (AUC: 0.86, ACC: 80.31%). Features including vascularization, irregular shape, ill-defined border, and larger size exhibited the highest diagnostic value for distinguishing MMBCs from FAs (AUC: 0.90, ACC: 87.43%). The comprehensive models showed excellent clinical value in distinguishing PMBCs (AUC = 0.86, SEN = 86.15%, SPE = 73.44%, ACC = 77.72%) and MMBCs (AUC = 0.92, SEN = 80.85%, SPE = 95.31%, ACC = 91.43%) from FAs. CONCLUSION: This diagnostic model holds promise for effectively distinguishing PMBCs and MMBCs from FAs, assisting radiologists in mitigating diagnostic biases and enhancing diagnostic efficiency.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

Background: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results: A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions: The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

Structural and Functional Alterations of Hippocampal Subfields in Patients With Adult-Onset Primary Hypothyroidism.

CONTEXT: Hypothyroidism is often associated with cognitive and emotional dysregulation; however, the underlying neuropathological mechanisms remain elusive. OBJECTIVE: The study aimed to characterize abnormal alterations in hippocampal subfield volumes and functional connectivity (FC) in patients with subclinical hypothyroidism (SCH) and overt hypothyroidism (OH). METHODS: This cross-sectional observational study comprised 47 and 40 patients with newly diagnosed adult-onset primary SCH and OH, respectively, and 53 well-matched healthy controls (HCs). The demographics, clinical variables, and neuropsychological scale scores were collected. Next, the hippocampal subfield volumes and seed-based FC were compared between the groups. Finally, correlation analyses were performed. RESULTS: SCH and OH exhibited significant alterations in cognitive and emotional scale scores. Specifically, the volumes of the right granule cell molecular layer of the dentate gyrus (GC-ML-DG) head, cornu ammonis (CA) 4, and CA3 head were reduced in the SCH and OH groups. Moreover, the volumes of the right molecular layer head, CA1 body, left GC-ML-DG head, and CA4 head were lower in SCH. In addition, the hippocampal subfield volumes decreased more significantly in SCH than OH. The seed-based FC decreased in SCH but increased in OH compared with HCs. Correlation analyses revealed thyroid hormone was negatively correlated with FC values in hypothyroidism. CONCLUSION: Patients with SCH and OH might be at risk of cognitive decline, anxiety, or depression, and exhibited alterations in volume and FC in specific hippocampal subfields. Furthermore, the reduction in volume was more pronounced in SCH. This study provides novel insights into the neuropathological mechanisms of brain impairment in hypothyroidism.

Alterations in Gray Matter Morphology and Functional Connectivity in Adult Patients with Newly Diagnosed Untreated Hypothyroidism.

Background: Untreated adult hypothyroidism may be associated with cognitive and emotional impairment, but the precise underlying neuropathological mechanism is unknown. We investigated the brain morphological and functional abnormalities associated with cognition and emotion in hypothyroidism. Methods: This is a cross-sectional observational study. Forty-four newly diagnosed adult hypothyroid patients and 54 well-matched healthy controls (HCs) were enrolled. All participants underwent three-dimensional T1-weighted imaging and resting-state functional magnetic resonance imaging (MRI). Morphological and seed-based functional connectivity (FC) analyses were performed to compare the intergroup differences. Neuropsychological tests, including the Montreal Cognitive Assessment (MoCA) Scale, 24-item Hamilton Depression Rating Scale (HAMD-24), and Hamilton Anxiety Rating Scale (HAMA) were administered. Thyroid function test and blood lipid levels were measured. Correlations were computed between neuropsychological and biochemical measures with neuroimaging indices. Sensitive morphological or functional neuroimaging indicators were identified using receiver operating characteristic (ROC) analysis. Results: Compared with HCs, hypothyroid patients demonstrated lower total and subdomain scores on the MoCA and higher HAMD-24 and HAMA scores. Morphological analysis revealed the hypothyroid patients had significantly reduced gray matter (GM) volumes in the right superior frontal gyrus, superior temporal gyrus, left dorsolateral superior frontal gyrus, middle frontal gyrus, and supplementary motor area as well as significantly increased GM volumes in the bilateral cerebellar Crus I and left precentral gyrus. Furthermore, seed-based FC analysis of hypothyroid patients showed increased FC between the right cerebellar Crus I and left precentral gyrus, triangular part of the inferior frontal gyrus, and angular gyrus of the inferior parietal lobe. The language scores of the MoCA were positively correlated with Jacobian values of the left supplementary motor area (r = 0.391, p = 0.046) and precentral gyrus (r = 0.401, p = 0.039). ROC analysis revealed FC value between cerebellar Crus I and angular gyrus could differentiate groups with relatively high accuracy (sensitivity: 75%, specificity: 77.8%, area under the curve: 0.794 [CI 0.701-0.888], p < 0.001). Conclusions: Untreated adult-onset hypothyroidism may be associated with impaired cognition and anxiety or depression. GM morphological alterations and FC of the cerebellum with subregions of the frontal and parietal lobes may represent key neuropathological mechanisms underlying the cognitive deterioration and mood dysregulation observed in hypothyroid adults. Clinical Trial Registration Number: chiCTR2000028966.

Segmental Abnormalities of White Matter Microstructure in Primary Hypothyroidism Identified by Automated Fiber Quantification.

INTRODUCTION: Hypothyroidism leads to impaired white matter (WM) integrity, associated with cognitive/neuropsychiatric dysfunction. However, the specific segmental abnormalities of the fibers remain unexplored. Therefore, this study aimed to investigate whether the damage of the WM is limited to a specific segment or the entire bundle via diffusion metrics using automated fiber quantification. METHODS: A cross-sectional study was conducted on 31 hypothyroid patients and 28 healthy controls. Thyroid-related hormone levels, cognitive/neuropsychiatric function, and diffusion tensor image data were collected and analyzed. Correlation and random forest analyses were also performed. RESULTS: The mean fractional anisotropy (FA) values were reduced at the fiber tract level. The mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values were increased in several fiber tracts, i.e., cingulum cingulate (CC), anterior forceps of corpus callosum (CCF_A). Significant correlations were found between cognitive function and diffusion indicators such as the FA value of the left corticospinal tract and arcuate fasciculus (AF), the MD value of left CC, the RD value of left AF, the AD value of left CC, and CCF_A. The widespread microstructure disruption was spread on multiple specific segments of different tracts at the point-wise level. The random forest revealed that the accuracy of recognizing hypothyroid patients was 82.5%, with the anterior component of CCF_A having the most significant contribution. CONCLUSION: WM microstructural integrity impairments were found in multi-segments of the multiple fiber bundles in hypothyroidism, which might be a potential mechanism of the underlying neurocognitive decline and cerebral impairment. The CCF_A might serve as a neuro biomarker for early warning of cerebral impairment in hypothyroidism.

Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism.

CONTEXT: Clinical hypothyroidism (CH) and subclinical hypothyroidism (SCH) have been linked to various metabolic comorbidities but the underlying metabolic alterations remain unclear. Metabolomics may provide metabolic insights into the pathophysiology of hypothyroidism. OBJECTIVE: We explored metabolic alterations in SCH and CH and identify potential metabolite biomarkers for the discrimination of SCH and CH from euthyroid individuals. METHODS: Plasma samples from a cohort of 126 human subjects, including 45 patients with CH, 41 patients with SCH, and 40 euthyroid controls, were analyzed by high-resolution mass spectrometry-based metabolomics. Data were processed by multivariate principal components analysis and orthogonal partial least squares discriminant analysis. Correlation analysis was performed by a Multivariate Linear Regression analysis. Unbiased Variable selection in R algorithm and 3 machine learning models were utilized to develop prediction models based on potential metabolite biomarkers. RESULTS: The plasma metabolomic patterns in SCH and CH groups were significantly different from those of control groups, while metabolite alterations between SCH and CH groups were dramatically similar. Pathway enrichment analysis found that SCH and CH had a significant impact on primary bile acid biosynthesis, steroid hormone biosynthesis, lysine degradation, tryptophan metabolism, and purine metabolism. Significant associations for 65 metabolites were found with levels of thyrotropin, free thyroxine, thyroid peroxidase antibody, or thyroglobulin antibody. We successfully selected and validated 17 metabolic biomarkers to differentiate 3 groups. CONCLUSION: SCH and CH have significantly altered metabolic patterns associated with hypothyroidism, and metabolomics coupled with machine learning algorithms can be used to develop diagnostic models based on selected metabolites.