An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis

Use of radiologic imaging to differentiate lipoma from atypical lipomatous tumor/well-differentiated liposarcoma: Systematic review

Background

Lipomas and atypical lipomatous tumors or well-differentiated liposarcomas (ALTs/WDLs), pose a diagnostic challenge due to their overlapping clinical and imaging features. Accurate differentiation is crucial as treatment strategies differ significantly between benign lipomas and malignant ALTs/WDLs. In recent years, medical imaging techniques have shown promise in distinguishing lipomas from ALTs/WDLs by providing enhanced visualization and assessment of various imaging parameters.

Objective

This systematic review aimed to investigate the use of magnetic resonance (MR) imaging and computed tomography (CT) scan to differentiate lipomas from ALTs/WDLs.

Methods

A systematic review was conducted by using MEDLINE, PubMed, PubMed Central, Cochrane Library, Google Scholar, and clinical trail.gov to identify imaging studies published between 2001 and 2022. Two independent reviewers reviewed 221 record to scrutinize the studies. The methodological quality of each included studies was assessed the using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool.

Results

Thirteen retrospective cohort studies included 1,390 of total patients. Among them, 11 studies used MR imaging, 2 studies used CT scan and MR imaging both to differentiate lipoma from ALTs/WDLs. The significant diagnostic variables identified in the included studies were age, size, texture, mean intensity, contrast enhancement, location, septation, and nodularity. The overall, sensitivity, specificity, and accuracy of the included studies for diagnosis of lesions range from 66% to 100%, 37% to 100%, and 76% to 95%, respectively. The positive and negative predictive values range from 46.9% to 90% and 86% to 100%, respectively.

Conclusion

The most frequent diagnostic features of ALTs/ WDLs include tumors ⩾110 mm in size, often in patients over 60, predominantly in the lower extremities, with an irregular shape, incomplete fat suppression, contrast enhancement, nodularity, septation >2 mm, and predictive markers such as lactate dehydrogenase >220 and a short tau inversion recovery-signal intensity ratio >1.18.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

Applications and challenges of neural networks in otolaryngology (Review)

Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.

A case report of giant paratesticular myxoid liposarcoma

Liposarcomas are an uncommon occurrence in the paratesticular region that makes about 20 % of all sarcomas. The clinical appearance is an inguinal lump, which can resemble a hydrocele or hernia. There would be no conventional treatment accessible because it is such a rare disease. We report the case of a 68-year-old man with paratesticular myxoid liposarcoma. Ultrasound and CT-scan came back in favor of a paratesticular tumor. A high inguinal orchidectomy has been done and the diagnostic of myxoid liposarcoma was first evoked by histology and confirmed by molecular biology. At 12 months follow up the patient remains tumor free.

Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods

BACKGROUND

Accurate and prompt diagnosis of the different patterns for pulmonary fibrosis is essential for patient management. However, accurate diagnosis of the specific pattern is challenging due to overlapping radiographic characteristics.

MATERIALS AND METHODS

We conducted a retrospective chart review utilizing two machine learning methods, classification and regression tree and Bayesian additive regression tree, to select the most important radiographic features for diagnosing the three most common fibrosis patterns and created an online diagnostic app for convenient implementation.

RESULTS

Four hundred patients (median age of 67 with inter quartile range 58-73; 200 males) were included in the study. Peripheral distribution, homogeneity, lower lobe predominance and mosaic attenuation of fibrosis are the four most important features identified. Bayesian additive regression tree demonstrates better performance than classification and regression tree in diagnosis prediction and provides the predicted probability of each diagnosis with uncertainty intervals for each combination of features.

CONCLUSION

The model and app built with Bayesian additive regression tree can be used as an effective tool in assisting radiologists in the diagnostic process of pulmonary fibrosis pattern recognition.

Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis

OBJECTIVES

This systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions.

METHODS

MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479.

RESULTS

Twenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63 years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10 cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing.

CONCLUSION

Higher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs.

CLINICAL RELEVANCE

This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities.

KEY POINTS

• The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10 cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.

Adding automated decision-tree models to multiparametric MRI for parotid tumours improves clinical performance

PURPOSE

Therapeutic management of parotid gland tumours depends on their histological type. To aid its characterisation, we sought to develop automated decision-tree models based on multiparametric magnetic resonance imaging (MRI) parameters and to evaluate their added diagnostic value compared with morphological sequences.

METHODS

206 MRIs from 206 patients with histologically proven parotid gland tumours were included from January 2009 to January 2018. Multiparametric MRI findings (including parameters derived from diffusion-weighted imaging [DWI] and dynamic contrast-enhanced [DCE]) were used to build predictive classification and regression tree (CART) models for each histological type. All MRIs were read twice: first, based on morphological sequence findings only, and second, with the addition of multiparametric sequences and CART findings. The diagnostic performance between these two readings was compared using ROC curves.

RESULTS

Compared to morphological sequences alone, the addition of multiparametric analysis significantly increased the diagnostic performance for all histological types (p < 0.001 to p = 0.011), except for lymphomas, where the increase was not significant (AUC 1.00 vs. 0.99, p = 0.066). ADCmean was the best parameter to identify pleomorphic adenomas, carcinomas and lymphomas with respective cut-offs of 1.292 × 10-3 mm2/s, 1.181 × 10-3 mm2/s and 0.611 × 10-3 mm2/s, respectively. × 10-3 mm2/s. The mean extracellular-extravascular space coefficient was the best parameter to Warthin tumours from the others, with a cut-off of 0.07.

CONCLUSIONS

The addition of decision tree prediction models based on multiparametric sequences improves the non-invasive diagnostic performance of parotid gland tumours. ADC and extracellular-extravascular space coefficient are the two best parameters for decision making.

Case Report: Giant paratesticular liposarcoma was resected and refused radical orchiectomy

Paratesticular liposarcoma (PLS) causes scrotal mass changes, rarely in the urinary system. Before surgery, PLS causes scrotal mass changes that are difficult to distinguish from other causes. There has been a report of a giant paratestis liposarcoma resection and refusal to undergo orchiectomy. A 65-year-old man presented with finding the left scrotal mass after 2 years. Physical examination showed that the left scrotal mass was obviously difficult to retract. Pelvic CT showed that the left scrotal mass and flaky fat density shadow accompanied with left inguinal hernia. During surgery, laparoscopic exploration was performed to rule out inguinal hernia, and a scrotal exploration was also performed concurrently. The intraoperative frozen pathology considered lipogenic tumor, and the patient's wife refused to undergo simultaneous left radical orchiectomy. Later the mass was completely removed, and postoperative pathology confirmed paratestis liposarcoma. During a 15-month routine follow-up, the tumor did not recur locally or metastasize distantly. PLS should be focused on early diagnosis and treatment, preoperative examinations and postoperative pathology should be combined, and highly personalized treatment will be implemented.

Evaluation of Classic and Quantitative Imaging Features in the Differentiation of Benign and Atypical Lipomatous Soft Tissue Tumors Using a Standardized Multiparametric MRI Protocol: A Prospective Single-Centre Study in 45 Patients

Background: Discrimination between benign and atypical lipomatous tumors (ALT) is important due to potential local complications and recurrence of ALT but can be difficult due to the often-similar imaging appearance. Using a standardized MRI protocol, this study aimed to rank established and quantitative MRI features by diagnostic value in the differentiation of benign and atypical lipomatous tumors and to develop a robust scoring system. Methods: Patients with clinical or sonographic suspicion of a lipomatous tumor were prospectively and consecutively enrolled from 2015 to 2019 after ethic review board approval. Histology was confirmed for all ALT and 85% of the benign cases. Twenty-one demographic and morphologic and twenty-three quantitative features were extracted from a standardized MRI protocol (T1/T2-proton-density-weighting, turbo-inversion recovery magnitude, T2* multi-echo gradient-echo imaging, qDIXON-Vibe fat-quantification, T1 relaxometry, T1 mapping, diffusion-weighted and post-contrast sequences). A ranking of these features was generated through a Bayes network analysis with gain-ratio feature evaluation. Results: Forty-five patients were included in the analysis (mean age, 61.2 ± 14.2 years, 27 women [60.0%]). The highest-ranked ALT predictors were septation thickness (gain ratio merit [GRM] 0.623 ± 0.025, p = 0.0055), intra- and peritumoral STIR signal discrepancy (GRM 0.458 ± 0.046, p < 0.0001), orthogonal diameter (GRM 0.554 ± 0.188, p = 0.0013), contrast enhancement (GRM 0.235 ± 0.015, p = 0.0010) and maximum diameter (GRM 0.221 ± 0.075, p = 0.0009). The quantitative features did not provide a significant discriminatory value. The highest-ranked predictors were used to generate a five-tiered score for the identification of ALTs (correct classification rate 95.7% at a cut-off of three positive items, sensitivity 100.0%, specificity 94.9%, likelihood ratio 19.5). Conclusions: Several single MRI features have a substantial diagnostic value in the identification of ALT, yet a multiparametric approach by a simple combination algorithm may support radiologists in the identification of lipomatous tumors in need for further histological assessment.

Differences in MRI findings of superficial spindle cell lipoma and atypical lipomatous tumor/well-differentiated liposarcoma

OBJECTIVE

This study aimed to evaluate the efficacy of using MRI findings to differentiate superficial spindle cell lipomas (SCLs) from atypical lipomatous tumor/well-differentiated liposarcomas (ALT/WDLs).

METHODS

This study included 12 patients with histopathologically proven superficial SCLs and 11 with ALT/WDLs. MRI findings for both pathologies were retrospectively reviewed and compared between the two pathologies.

RESULTS

The neck, upper back, and shoulder regions were more frequent locations of SCLs than of ALT/WDLs (100% vs 55%, p < 0.05), whereas no significant differences were observed in age and sex. The median maximum diameter of the lesion was smaller in SCLs than in ALT/WDLs (44 mm [interquartile range (IQR): 35-63] vs 102 mm [IQR: 86-119], p < 0.05). On T 1 weighted images, non-fatty area was more frequently observed in SCLs than in ALT/WDLs (73% vs 25%, p < 0.05), and the median rate of non-fatty area was larger in SCLs than in ALT/WDLs (7.5% [IQR: 1.0-53] vs 0% [IQR: 0-0.2], p < 0.05). On fat-suppressed T 2 weighted images, a solid hyperintense area was more frequently observed in SCLs than in ALT/WDLs (83% vs 27%, p < 0.05).

CONCLUSION

The maximum diameter, non-fatty area on T 1 weighted images, and solid hyperintense area on fat-suppressed T 2 weighted images were useful imaging features for differentiating superficial SCLs from ALT/WDLs.

ADVANCES IN KNOWLEDGE

In superficial lipomatous tumors, small tumor size and non-fatty solid area were valuable findings for diagnosing SCLs.

MDM4 amplification in atypical lipomatous tumors/well-differentiated liposarcoma: Private event or alternative oncogenic mechanism?

Adipocytic tumors are the most common mesenchymal tumors in soft tissues. Among them, a diagnostic challenge relies in the distinction between lipoma and atypical lipomatous tumor (ALT)/well differentiated liposarcoma (WDLPS), as both entities are often undistinguishable not only from a radiological point of view, but also at the microscopic level and particularly when dealing with small tumor specimen. Thus, detection of recurrent MDM2 amplifications may be the only criteria to discriminate malignant tumors from lipomas. In this study, we report the case of a patient diagnosed with a well differentiated, adipocytic tumor located in the inferior limb and lacking MDM2 amplification, whose diagnosis was reclassified for ALT/WDLPS after identification of an alternative MDM4 amplification by comparative genomic hybridization profiling, whole exome sequencing and fluorescence in situ hybridization (FISH). Screening of a cohort of 37 large, deep-seated, well-differentiated adipocytic tumors previously classified as lipomas using RT-qPCR and FISH failed to detect other cases of MDM4-amplified ALT/WDLPS. This report shows that MDM4 amplification is an exceptional molecular event alternative to MDM2 amplification in ALT/WDLPS. This alteration should be considered and looked for in suspicious adipocytic tumors to optimize their surgical management.

MRI findings to differentiate musculoskeletal dedifferentiated liposarcoma from atypical lipomatous tumor

OBJECTIVE

This study aimed to assess the efficacy of using MRI findings for differentiating musculoskeletal dedifferentiated liposarcoma (DDLP) from atypical lipomatous tumor (ALT).

MATERIALS AND METHODS

This study included 22 patients with histopathologically proven DDLP and 35 with ALT in the musculoskeletal areas. All DDLPs were immunohistochemically positive for MDM2. MRI findings for both pathologies were retrospectively reviewed and compared.

RESULTS

The maximum lesion diameter was significantly lower in DDLPs than in ALTs (p < 0.01). Ill-defined margin, peritumoral edema, and tail sign were more frequently observed in DDLPs than in ALTs (p < 0.01, respectively). The fatty component was less frequently observed in DDLPs than in ALTs (27 vs. 100%; p < 0.01), whereas the non-fatty component was more frequently observed in DDLPs than in ALTs (100 vs. 11%; p < 0.01). The occupation rate by non-fatty components was significantly higher in DDLPs than in ALTs (p < 0.01). No significant differences were observed in imaging findings associated with fatty component; however, necrosis within the non-fatty component on the contrast-enhanced image was more frequently observed in DDLPs than in ALTs (72 vs. 0%, p < 0.05).

CONCLUSION

DDLPs always had a non-fatty component, whereas ALTs always had fatty component. Ill-defined margin, peritumoral edema, tail sign, and necrosis within non-fatty components were useful MRI features for differentiating musculoskeletal DDLP from ALT.

Giant Paratesticular Liposarcoma: Molecular Characterization and Management Principles with a Review of the Literature

Paratesticular liposarcomas are extremely rare malignant tumors originating from fat tissues, with an often-challenging diagnosis. We present here the case of a 76-year-old man with a giant paratesticular liposarcoma, initially misdiagnosed as a scrotal hernia. After two years, the progressively enlarging mass underwent surgical resection, and a diagnosis of well-differentiated liposarcoma (lipoma-like subtype) was made. Post-operative treatments were not indicated, and the patient remains relapse free. Next generation sequencing performed on the neoplastic tissue showed co-amplification of MDM2 and CDK4. These alterations are molecular hallmarks of well-differentiated liposarcomas and corroborate the histological diagnosis. Clinical and molecular features of the presented case are in line with the majority of previously published experiences. In conclusion, the presence of a liposarcoma should be taken into account during the diagnostic workup of scrotal masses, in order to minimize the rate of misdiagnosis and improper management. Molecular analysis may support histological characterization of these rare entities and potentially disclose novel therapeutic targets.

Differentiation of multiple myelomas from osteolytic bone metastases: Diagnostic value of tumor homogeneity on Contrast-Enhanced CT

OBJECTIVES

To investigate the diagnostic value of tumor homogeneity on contrast-enhanced (CE) computed tomography (CT) to differentiate multiple myeloma (MM) from osteolytic bone metastases (Mets).

METHODS

This retrospective study included patients who were diagnosed with MM or Mets and had multiple (≥2) osteolytic bone tumors on pre-treatment CE-CT. Intratumoral homogeneity was assessed by coefficient of variation (CV, ratio of standard deviation to mean) of the density of a single lesion (CV-lesion). Intertumoral homogeneity was assessed as the CV of the densities of multiple lesions in one patient (CV-patient). A classification model was built from CT parameters using classification and regression tree (CART) analysis. Diagnostic performance of the model was evaluated using C-statistics.

RESULTS

A total of 272 lesions (81 MM and 191 Mets) of 105 patients were analyzed. The mean CV-lesion and CV-patient of MM were significantly lower than those of Mets: 0.17 vs 0.26 for CV-lesion (p = 0.005) and 0.16 vs 0.23 for CV-patient (p = 0.013). Thickened struts were more common in MM than in Mets (49.1% vs 12.8%, p ≤ 0.001). In CART analysis, CV-lesion was the first partitioning predictor, followed by thickened struts and by CV patient. The CART model could distinguish MM from Mets in both the model development cohort (C-statistic: 0.843) and the temporal validation cohort (0.721, 0.686, and 0.686 for three reviewers, respectively).

CONCLUSIONS

MM showed intratumoral and intertumoral homogeneity compared with Mets on CE-CT. The combination of CV-lesion and CV-patient can be helpful to radiologists in differentiation of MM from Mets.

ADVANCES IN KNOWLEDGE

Our study showed that MM had intratumoral and intertumoral homogeneity compared with Mets on contrast-enhanced CT.

Application of a Random Forest Algorithm in Natural Landscape Animation Design

Natural landscape simulation is one of the most popular research contents in computer graphics in the field of research simulation system. The natural landscape animation scene can immerse viewers in the scene, and it is widely used in visual simulation systems. Simulating natural scenery on a computer is a powerful method for studying the rules of the scenery's growth process as well as the mystery of life. The simulation of natural scenery is of particular importance and has far-reaching implications. The most important aspect of optimizing natural landscape design is creating a natural landscape animation that users enjoy. This article proposes a natural landscape animation design method with a self-learning function based on this concept. The random forest model (RF) is introduced in this method and applied to the entire animation design process. RF can generate a learning model with user evaluation as the classification result to guide the automatic design of natural landscape animation, resulting in user-satisfying animations. Simultaneously, the RF-based natural landscape animation design can continuously update the learning model based on user needs and is self-learning. The experimental part of this article verifies the effectiveness of the natural landscape animation design proposed in this article by comparing the selection rate of user satisfaction and dissatisfaction scenes, and further demonstrates that the method in this article can improve the natural landscape. The market application value of user satisfaction generated by animation is high.

Diagnosis strategy of adipocytic soft-tissue tumors in adults: a consensus from European experts

Fat-containing tumors are very commonly found in daily practice with benign lipoma accounting for the majority of superficial tumors. Overlap in imaging findings between benign and intermediate or malignant fat-containing tumor is common. Guidelines recommend a core needle biopsy (CNB) for all deep tumors, and superficial tumors over 3 cm. However, specific strategy for diagnosis and referral to a sarcoma center should be applied on adipocytic tumors. The aim of this consensus statement is to provide a specific algorithm for adipocytic tumors, to discriminate patients who do require a CNB for preoperative diagnosis from those who can simply undergo active surveillance or a simple excision.

Differential diagnosis of T2 hypointense masses in musculoskeletal MRI

Many soft tissue masses have an indeterminate appearance on MRI, often displaying varying degrees and extent of T2 hyperintensity. However, a subset of neoplasms and tumor-like lesions may exhibit prominent areas of T2 hypointensity relative to skeletal muscle. The hypointensity observed on T2-weighted MRI can be caused by a variety of substances, including evolving blood products, calcifications or other inorganic crystals, or fibrous tissue. Carefully evaluating the presence and pattern of T2 hypointensity in soft tissue masses and considering potential causes in their associated clinical contexts can help to narrow the differential diagnosis among neoplastic and non-neoplastic possibilities. These include endometriosis, aneurysmal bone cysts, tenosynovial giant cell tumor, arteriovenous malformation and pseudoaneurysm, calcium pyrophosphate and hydroxyapatite deposition diseases, tumoral calcinosis, gout, amyloidosis, hemangiomas with phleboliths, low-grade fibromyxoid sarcoma, ossifying fibromyxoid tumor, collagenous fibroma, desmoid-type fibromatosis, myxofibrosarcoma, peripheral nerve sheath tumors, dedifferentiated liposarcoma, and treated sarcoma.

Serum Biomarkers and Classification and Regression Trees Can Discriminate Symptomatic from Asymptomatic Carotid Artery Disease Patients

Objective

To assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination.

Patients and Methods

136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated.

Results

All measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation.

Conclusion

Significant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease.

Contribution of structural accessibility to the cooperative relationship of TF-lncRNA in myopia

Transcriptional regulation is associated with complicated mechanisms including multiple molecular interactions and collaborative drive. Long noncoding RNAs (lncRNAs) have highly structured characteristics and play vital roles in the regulation of transcription in organisms. However, the specific contributions of conformation feature and underlying molecular mechanisms are still unclear. In the present paper, a hypothesis regarding molecular structure effect is presented, which proposes that lncRNAs fold into a complex spatial architecture and act as a skeleton to recruit transcription factors (TF) targeted binding, and which is involved in cooperative regulation. A candidate set of TF-lncRNA coregulation was constructed, and it was found that structural accessibility affected molecular binding force. In addition, transcription factor binding site (TFBS) regions of myopia-related lncRNA transcripts were disturbed, and it was discovered that base mutations affected the occurrence of significant molecular allosteric changes in important elements and variable splicing regions, mediating the onset and development of myopia. The results originated from structureomics and interactionomics and created conditions for systematic research on the mechanisms of structure-mediated TF-lncRNA coregulation in transcriptional regulation. Finally, these findings will help further the understanding of key regulatory roles of molecular allostery in cell physiological and pathological processes.