Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.

OBJECTIVE: The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models. METHODS: PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity. RESULTS: Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively. CONCLUSIONS: Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future. ADVANCES IN KNOWLEDGE: The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.

Effects of inflammation, childhood adversity, and psychiatric symptoms on brain morphometrical phenotypes in bipolar II depression.

BACKGROUND: The neuroanatomical alteration in bipolar II depression (BDII-D) and its associations with inflammation, childhood adversity, and psychiatric symptoms are currently unclear. We hypothesize that neuroanatomical deficits will be related to higher inflammation, greater childhood adversity, and worse psychiatric symptoms in BDII-D. METHODS: Voxel- and surface-based morphometry was performed using the CAT toolbox in 150 BDII-D patients and 155 healthy controls (HCs). Partial Pearson correlations followed by multiple comparison correction was used to indicate significant relationships between neuroanatomy and inflammation, childhood adversity, and psychiatric symptoms. RESULTS: Compared with HCs, the BDII-D group demonstrated significantly smaller gray matter volumes (GMVs) in frontostriatal and fronto-cerebellar area, insula, rectus, and temporal gyrus, while significantly thinner cortices were found in frontal and temporal areas. In BDII-D, smaller GMV in the right middle frontal gyrus (MFG) was correlated with greater sexual abuse (r = -0.348, q < 0.001) while larger GMV in the right orbital MFG was correlated with greater physical neglect (r = 0.254, q = 0.03). Higher WBC count (r = -0.227, q = 0.015) and IL-6 levels (r = -0.266, q = 0.015) was associated with smaller GMVs in fronto-cerebellar area in BDII-D. Greater positive symptoms was correlated with larger GMVs of the left middle temporal pole (r = 0.245, q = 0.03). CONCLUSIONS: Neuroanatomical alterations in frontostriatal and fronto-cerebellar area, insula, rectus, temporal gyrus volumes, and frontal-temporal thickness may reflect a core pathophysiological mechanism of BDII-D, which are related to inflammation, trauma, and psychiatric symptoms in BDII-D.

Brain-derived subgroups of bipolar II depression associate with inflammation and choroid plexus morphology.

AIM: Elevated inflammation and larger choroid plexus (ChP) volume has been previously identified in mood disorders. Connections between inflammation, ChP, and clinical symptoms in bipolar II depression (BDII-D) are unclear. Data-driven clustering based on neuroanatomical phenotypes may help to elucidate neurobiological associations in BDII-D. METHODS: Inflammatory cytokines, clinical symptoms, and neuroanatomical features were assessed in 150 BDII-D patients. Sixty-eight cortical surface area (SA) and 19 subcortical volumes were extracted using FreeSurfer. The ChP volume was segmented manually using 3D Slicer. Regularized canonical correlation analysis was used to identify significantly correlated components between cortical SA and subcortical volumes (excluding the ChP), followed by k-means clustering to define brain-derived subgroups of BDII-D. Low-grade inflammation was derived by averaging the standardized z scores of interleukin (IL)-6, IL-1ß, and tumor necrosis factor-α (TNF-α), which were computed to create a composite z-value score. Partial Pearson correlations followed by multiple comparison correction were conducted to explore associations between inflammation, clinical symptoms, and ChP volume. RESULTS: Subgroup I demonstrated smaller subcortical volume and cortical SA, higher inflammation, and larger ChP volume compared with subgroup II. Greater ChP volume was associated with a higher low-grade inflammation (mean r = 0.289, q = 0.003), CRP (mean r = 0.249, q = 0.007), IL-6 (left r = 0.200, q = 0.03), and TNF-α (right r = 0.226, q = 0.01), while greater IL-1ß was significantly associated with severe depressive symptoms in BDII-D (r = 0.218, q = 0.045). CONCLUSIONS: Neuroanatomically-derived subgroups of BDII-D differed in their inflammation levels and ChP volume. These findings suggest an important role of elevated peripheral inflammation and larger ChP in BDII-D.

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.

Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.