Giá trị của siêu âm sử dụng bảng phân loại ACR - TIRADS 2017 trong chẩn đoán nhân giáp

TÓM TẮT Đặt vấn đề: Bệnh lý nhân giáp là một bệnh lý phổ biến, đặc biệt là ở phụ nữ và người lớn tuổi. Siêu âm tuyến giáp, được xem như là một phương tiện đầu tay, là phương pháp chẩn đoán hình ảnh có những khả năng vượt trội như tương đối đơn giản, rẻ tiền, không xâm lấn, có thể lặp lại nhiều lần để chẩn đoán bệnh, và có khả năng phát hiện được những tổn thương rất nhỏ. Nghiên cứu này nhằm xác định xác giá trị của siêu âm sử dụng bảng phân loại ACR-TIRADS 2017 trong chẩn đoán nhân giáp. Phương pháp: Thiết kế nghiên cứu mô tả cắt ngang, với cỡ mẫu 169 bệnh nhân được phẫu thuật nhân giáp. Trước phẫu thuật, bệnh nhân được siêu âm tuyến giáp bằng máy GE (LOGIQ S7 Pro, LOGIQ E9 …) với đầu dò linear tần số 7,5 - 12 MHz. Kết quả siêu âm bảng phân loại TI-RADS theo ACR 2017 so sánh với tiêu chuẩn vàng là kết quả giải phẫu bệnh. Kết quả: Siêu âm áp dụng bảng phân loại ACR-TIRADS 2017 trong phân biệt nhân giáp lành tính và ác tính: Độ nhạy 97,9%, độ đặc hiệu 82,6%, giá trị tiên đoán dương 95,8%, giá trị tiên đoán âm 90,5%, và độ chính xác 94,9%. Diện tích dưới đường cong ROC (AUC) của phân loại ACR-TIRADS trong chẩn đoán nhân giáp ác tính là bằng 0,953 (p < 0,001). Điểm cắt (cut - off) được chọn là TIRADS 4. Diện tích dưới đường cong ROC (AUC) của điểm số của hạt giáp theo phân loại ACR- là 0,967 (p < 0,001). Điểm cắt (cut - off) được chọn là 5 điểm. Kết luận: Siêu âm áp dụng bảng phân loại ACR-TIRADS 2017 có giá trị trong chẩn đoán phân biệt nhân giáp lành tính và ác tính với độ nhạy và độ đặc hiệu cao. ABSTRACT THE USE OF THYROIDULTRASOUND WITH ACR - TIRADS 2017 CLASSIFICATION IN THE DIAGNOSIS OF THYROID NODULES Backgrounds: Thyroid disease is very common, especially in women and the elderly. Thyroid ultrasound, as a first - line tool, is an imaging modality with outstanding capabilities such as being relatively simple, inexpensive, non - invasive, and repeatable for diagnosis of thyroid diseases, and can detect very small lesions. This study aims to determine the use of thyroid ultrasound with ACR-TIRADS 2017 classification in the diagnosis of thyroid nodules. Methods: A cross - sectional descriptive study was conducted in 169 patients undergoing thyroidectomy. All patients had been preoperatively performed thyroid ultrasound using a GE machine (LOGIQ S7 Pro, LOGIQ E9 ...) with a linear transducer frequency of 7.5 - 12 MHz. The ultrasound results using the 2017 ACR-TIRADS classification compared with pathological findings as the gold standard diagnostics. Results: Thyroid ultrasound using the 2017 ACR-TIRADS classification could distinguish benign and malignant thyroid nodules with the sensitivity of 97.9%, specificity 82.6%, positive predictive value 95.8%, negative predictive value 90.5%, and accuracy of 94.9%. The area under the ROC curve (AUC) of the ACRTIRADS classification in the diagnosis of malignant thyroid nodules was 0.953 (p < 0.001). The cut - off point was selected as TIRADS 4. The area under the ROC curve (AUC) of the ACR - classification score of the armor particles was 0.967 (p < 0.001). The cut - off point is selected as 5 points. Conclusion: Thyroid ultrasound using the 2017 ACR-TIRADS classification is valuable in the differential diagnosis of benign and malignant thyroid nodules with high sensitivity and specificity. Keywords: Ultrasound, thyroid nodules, ACR-TIRADS 2017, benign, malignant.

Predictive Value of Preoperative MRI Using the #ENZIAN Classification Score in Patients with Deep Infiltrating Endometriosis

Purpose The aim of this study is to investigate the correlation between the magnetic resonance imaging (MRI) and intraoperative findings of deep infiltrating endometriosis using the #ENZIAN score. Methods This retrospective study included 64 patients who underwent surgery for deep infiltrating endometriosis between January 2017 and August 2020. Preoperative abdominopelvic MRI assessment was evaluated and scored using the #ENZIAN classification. Operative scores were considered the gold standard, and the sensitivity, specificity, and positive and negative predictive values (PPV and NPV) of MRI for each category were calculated. Results MRI has higher sensitivity and specificity in showing the lesions of the compartments O (ovarian lesions), A (rectovaginal septum and posterior vaginal fornix), and B (uterosacral ligaments and parametrium) (100–100%, 100–100%, and 97–100%, respectively, p<0.001) compared to the other compartments. The lowest sensitivity, specificity, accuracy, and PPV of the MRI was found in compartment P (14%, 76%, 70%, and 7%, respectively). Conclusion We demonstrated that the #ENZIAN classification in MRI reports has significant sensitivity and specificity in compartments A, B (uterosacral ligaments and parametrium), and O. Furthermore, the determination of peritoneal lesions via MRI is inadequate.

Radiological Outcomes of Femoral Head Resection in Patients with Cerebral Palsy: A Retrospective Comparative Study of Two Surgical Procedures

Background: We conducted this study to compare postoperative radiological outcomes of two surgical procedures (femoral head resection (FHR) and femoral head cap plastic surgery (FCP)) in patients with CP and hip dislocation. Methods: CP patients with Gross Motor Function Classification Score (GMFCS) IV or V, who underwent either FHR or FCP between 2007 and 2018 at Heidelberg University Hospital in Germany, were included. Most participants underwent postoperative traction in an attempt to prevent telescoping. Besides the above-mentioned objectives, we examined the association between telescoping and spasmolytic use, traction weight, and traction duration. Results: Thirty-eight CP patients were included, of whom 15 (25 hips) underwent FHR and 23 (30 hips) underwent FCP. Heterotopic ossification (grades I, II, and III) occurred in 80% and 83.3% of patients in the FHR and FCP groups, respectively. Telescoping occurred in 18.68 and 31.99% of patients in the FHR and FCP groups, respectively (p = 0.999). Other complications were similar between both groups. Conclusions: The postoperative outcomes of FHR and FCP are similar in terms of telescoping, heterotopic ossification, and complications. Although telescoping was encountered more in the FCP group, no significant difference from the FHR group was found. We noted that the weight of traction could reduce the development of telescoping.

Scene Text Detection Based on Expanding the Text Center Region for Bilingual Tibetan-Chinese

Scene text detection is an important research branch of artificial intelligence technology whose goal is to locate text in scene images. In the Tibetan areas of China, scene images containing both Tibetan and Chinese texts are ubiquitous. Thus, detecting bilingual Tibetan-Chinese scene texts is important in promoting intelligent applications for minority languages. In this study, a scene text detection database for bilingual Tibetan-Chinese is constructed using a manually labeled method and an automatic synthesis method, and a text detection method is proposed. First, we predict a text rectangular region and the text center region for each text instance and simultaneously learned the expansion distance of the text center region. Second, based on the classification score of the text center region and the text rectangular region, we obtain the final confidence of each text instance and then filter out the text center region with lower confidence. Third, through the learned expansion distance, the full-text instance from the remaining text center region is recovered. The results show that our method obtains good detection performance; it achieves an accuracy of up to 75.47% during the text detection phase, laying the foundation for scene text recognition in the subsequent step.

Deep Learning Approach for Voice Pathology Detection and Classification

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.

Impact of cone beam CT on diagnosis of external cervical resorption: the severity of resorption assessed in periapical radiographs and cone beam CT. A prospective clinical study

Objectives: To compare the severity of external cervical resorption (ECR) observed in periapical (PA) images and cone beam CT (CBCT) using the Heithersay classification system and pulp involvement; and to assess inter- and intraobserver reproducibility for three observers. Methods: CBCT examination was performed in 245 teeth (in 190 patients, mean age 40 years, range 12–82) with ECR diagnosed in PA images. Three observers scored the severity of ECR using the Heithersay classification system (severity class 1–4) and pulp involvement (yes/no) in both PA images and CBCT. Percentage concordance and κ-statistics described observer variation in PA images and CBCT for both inter- and intraobserver reproducibility. Results: For all three observers, the ECR score was the same in the two modalities in more than half of cases (average 59%; obs1: 54%, obs2: 63%, obs3: 61%). However, in 38% (obs1: 44%, obs2: 33%, obs3: 36%) of the cases, the observers scored more severe ECR in CBCT than in PA images (p < 0.001). The ECR score changed to a less severe score in CBCT only in 3% (obs1: 1%, obs2: 4%, obs3: 4%). For pulp involvement, 14% (obs1: 7%, obs2: 20%, obs3: 15%) of the cases changed from “no” in PA images to “yes” in CBCT. In general, κ values were higher for CBCT than for PA images for both the Heithersay classification score and pulp involvement. Conclusions: ECR was generally scored as more severe in CBCT than PA images using the Heithersay classification and also more cases had pulp involvement in CBCT.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.