OBJECTIVE: To evaluate the accuracy of diffusion-weighted magnetic resonance imaging (DWI-MRI) in diagnosing persistent/recurrent head and neck squamous cell carcinomas (HNSCCs) after primary chemoradiotherapy (CRT).
METHODS: Scopus, PubMed/MEDLINE, and Cochrane Library databases were searched for relevant publications until April 18, 2023.
METHODS: A systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses of Diagnostic Test Accuracy statement. The search was conducted independently by 2 investigators. Methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Studies-2 questionnaire. Extracted data were used to calculate the pooled DWI-MRI sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratio.
RESULTS: A total of 618 patients from 10 studies were included for calculation of diagnostic accuracy parameters. At the level of the primary tumor, the pooled sensitivity and specificity were, respectively, 0.96 (95% confidence interval [CI]: 0.89-1.00) and 0.81 (95% CI: 0.54-0.98) in the case of qualitative analysis, and, respectively, 0.79 (95% CI: 0.66-0.89) and 0.88 (95% CI: 0.77-0.96) for quantitative analysis. At the level of the neck, the pooled sensitivity and specificity were, respectively, 0.87 (95% CI: 0.75-0.95) and 0.84 (95% CI: 0.74-0.93) when images were analyzed qualitatively, and 0.79 (95% CI: 0.60-0.94) and 0.90 (95% CI: 0.82-0.97) when analyzed quantitatively.
CONCLUSIONS: DWI-MRI showed high diagnostic accuracy and should be considered if persistent/recurrent HNSCCs is suspected after primary CRT. No significant differences were found between qualitative and quantitative imaging assessment.

BACKGROUND: Enamel is highly transparent at short wavelength infrared imaging (SWIR) wavelengths allowing the detection of dental decay without the need for ionizing radiation. The purpose of this study was to use SWIR imaging methods including cross polarization optical coherence tomography (CP-OCT), occlusal transillumination (SWIR-OT), proximal transillumination (SWIR-PT), and occlusal reflectance (SWIR-R) to image interproximal lesions in vivo and compare the sensitivity with radiography.
METHODS: Participants (n = 30) aged 18-80 each with a radiopositive interproximal lesion scheduled for restoration were enrolled in the study. Studies have shown that the opposing proximal surfaces across the contact will likely also have lesions. SWIR images were acquired of the adjoining teeth at each contact with an interproximal lesion scheduled for restoration. Lesion presence and depth were assessed on each side of the contact for radiography and each SWIR imaging method. Lesions on radiographs and in CP-OCT images were identified by a single examiner while lesions in SWIR images were identified by a contrast threshold via semi-automatic image segmentation.
RESULTS: All SWIR imaging methods had significantly higher sensitivity (P < 0.05) than radiographs for the detection of interproximal lesions on the teeth opposite those restored. CP-OCT and SWIR-R imaging methods had significantly higher sensitivity than the other methods. SWIR imaging methods showed significantly higher lesion contrast than radiography.
CONCLUSIONS: SWIR imaging methods can be used to detect interproximal lesions on posterior teeth with higher diagnostic performance than radiographs. CP-OCT appears well suited as a potential gold standard for the detection of interproximal lesions and assessment of their severity in vivo.

OBJECTIVE: Two-dimensional shear wave elastography (2D-SWE) is a non-invasive technique for the evaluation of Sjögren\'s disease (SjD). This study investigated the diagnostic accuracy of 2D-SWE in assessing major salivary gland involvement in SjD.
METHODS: A systematic review and meta-analysis.
METHODS: Data were obtained by searching PubMed, MEDLINE, EMBASE, Scopus, Cochrane library and CNKI from 1999 to 26 September 2022, which includes randomised clinical trial of 2D-SWE for the diagnosis of SjD.
METHODS: (1) Patients (≥18 years old) diagnosed with SjD following the international classification in 2002 or 2016 American College of Rheumatology-European League Against Rheumatism classification criteria for SjD; (2) The purpose of this study was to evaluate the diagnostic value of 2D-SWE in SjD; (3) The evaluation parameters for the diagnosis of SjD can be extracted or indirectly obtained in this article, including sensitivity, specificity, true positive, false positive, false negative, true negative, diagnostic point (Young\'s modulus) and other data.
METHODS: Four authors independently screened and assessed the literature and extracted the data. RevMan V.5.3 and StataMP V.18 software were used for quality assessment and meta-analysis.
RESULTS: We included 8 studies with a total of 912 cases, including 509 patients with SjD. The high-risk bias in the quality evaluation focused on patient selection and index test. The pooled sensitivity, specificity and summary area under the curve of 2D-SWE were 0.75 (95% CI 0.62 to 0.84), 0.89 (95% CI 0.80 to 0.94) and 0.90 (95% CI 0.87 to 0.92), respectively.
CONCLUSIONS: 2D-SWE has an acceptable diagnostic accuracy for SjD patients and is an effective tool for auxiliary diagnosis of SjD.
UNASSIGNED: CRD42022365766.

OBJECTIVE: The main aim was to determine the diagnostic performance of an albuminuria point-of-care test (POC) for diagnosis of chronic kidney disease among young people living with HIV (YPLHIV) in Uganda.
METHODS: We conducted a cross-sectional study comparing the diagnostic performance of MicroalbuPHAN (Erba Lachema, Czech Republic), an albuminuria POC test against the laboratory-measured albumin and creatinine as the reference standard.
METHODS: The study was set in seven HIV clinics in Kampala, Uganda that provide antiretroviral therapy to adults and children living with HIV. The study took place from April to August 2023.
METHODS: 497 YPLHIV aged 10-24 years who were diagnosed with HIV before 10 years of age were randomly selected from the HIV clinics. Pregnant YPLHIV were excluded.
METHODS: Participants provided a spot urine sample that was tested for albumin and creatinine using the POC and in the laboratory and proteinuria using urine dipstick. The sensitivity, specificity, negative and positive predictive values (NPV, PPV) of the POC versus the laboratory test were calculated, and factors associated with having a positive POC test were estimated using logistic regression.
METHODS: The primary outcome was a diagnosis of albuminuria defined as an albumin creatinine ratio above 30 mg/g.
RESULTS: Of the 497 participants enrolled, 278 (55.9%) were female and 331 (66.8%) were aged 10-17 years. The POC test had a sensitivity of 74.5% (95% CI 70.6% to 78.4%) and specificity of 68.1% (95% CI 63.9% to 72.3%). The PPV was 21.5% (95% CI 17.8% to 25.1%) and the NPV was 95.8% (95% CI 94.0% to 97.6%), with an accuracy of 68.8%. There was strong evidence that a positive POC test was associated with having proteinuria (OR 2.82; 95% CI 1.89 to 4.22, p<0.001); body mass index <19.5 (OR 1.69 95% CI 1.17 to 2.45, p=0.005) and being male (OR 1.48; 95% CI 1.02 to 2.14, p=0.04).
CONCLUSIONS: The albuminuria POC test had low sensitivity and specificity. However, it can be used to exclude kidney disease given its high NPV. It should be validated against the 24-hour urinary excretion rate to further determine its diagnostic performance.

OBJECTIVE: In this study, we aimed to validate the performance of the PAX1 and JAM3 methylation (PAX1m/JAM3m) test as a triage tool for detecting cervical intraepithelial neoplasia grade 3 or worse (CIN3 +) in non-16/18 high-risk human papillomavirus-positive patients (non-16/18 hrHPV +).
METHODS: The triage performance of liquid-based cytology (LBC) and the PAX1m/JAM3m test for detecting CIN3 + were compared.
RESULTS: In total, 1851 participants had cervical histological outcomes and were included in the analysis. The sensitivity/specificity of the LBC test results with atypical squamous cells of undetermined significance or worse (LBC ≥ ASCUS) and the PAX1m/JAM3m test were 90.1%/26.7% and 84.8%/88.5%, respectively. PAX1m/JAM3m( +) had the highest diagnostic AUC (0.866, 95% confidence interval (CI) 0.837-0.896) in the whole cohort. All cancers (n = 20) were detected by PAX1m/JAM3m(+). Compared with LBC ≥ ASCUS, PAX1m/JAM3m(+) reduced the number of patients who needed referral for colposcopy by 57.21% (74.66% vs. 17.45%). The odds ratios for detecting CIN3 + by LBC ≥ ASCUS and PAX1m/JAM3m(+) were 3.3 (95% CI 2.0-5.9) and 42.6 (27.1-69.6), respectively (p < 0.001). The combination of LBC ≥ ASCUS or PAX1m/JAM3m(+) slightly increased the diagnostic sensitivity (98.0%, 95% CI: 95.8-100%) and referral rate (77.09%) but reduced the diagnostic specificity (24.8%, 22.7-26.8%).
CONCLUSIONS: In non-16/18 hrHPV(+) women, PAX1m/JAM3m was superior to cytology for detecting CIN3 + . Compared with LBC ≥ ASCUS, PAX1m/JAM3m(+) reduced the number of significant referrals to colposcopy without compromising diagnostic sensitivity.

BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.
METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1.
RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively.
CONCLUSIONS: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Glioma represents the most common central nervous system neoplasm in adults. Current classification scheme utilizes molecular alterations, particularly IDH1.R132H, to stratify lesions into distinct prognostic groups. Identification of the single nucleotide variant through traditional tissue biopsy assessment poses procedural risks and does not fully reflect the heterogeneous and evolving tumor landscape. Here, we introduce a liquid biopsy assay, mt-IDH1dx. The blood-based test allows minimally invasive detection of tumor-derived extracellular vesicle RNA using only 2 ml plasma volume. We perform rigorous, blinded validation testing across the study population (n = 133), comprising of IDH1.R132H patients (n = 80), IDH1 wild-type gliomas (n = 44), and age matched healthy controls (n = 9). Results from our plasma testing demonstrate an overall sensitivity of 75.0% (95% CI: 64.1%-84.0%), specificity 88.7% (95% CI: 77.0%-95.7%), positive predictive value 90.9%, and negative predictive value 70.1% compared to the tissue gold standard. In addition to fundamental diagnostic applications, the study also highlights the utility of mt-IDH1dx platform for blood-based monitoring and surveillance, offering valuable prognostic information. Finally, the optimized workflow enables rapid and efficient completion of both tumor tissue and plasma testing in under 4 hours from the time of sampling.

Xpert MTB/RIF is recommended for the diagnosis of tuberculosis (TB) in children. We determined the performance of Xpert MTB/RIF in the diagnosis of pulmonary TB in children. The characteristics of children influencing Xpert MTB/RIF positivity were explored. Children aged <15 years with symptoms suggestive of pulmonary TB were prospectively enrolled from 2013 to 2019. Two sputum/early morning gastric aspirate specimens were collected for examination by smear (fluorescence microscopy), Xpert MTB/RIF, and culture [Mycobacteria growth indicator tube (MGIT)/Lowenstein-Jensen (LJ) medium]. Diagnostic performance of Xpert MTB/RIF was evaluated using LJ and or MGIT culture positivity as the reference standard. Sensitivity, specificity with 95% confidence interval (CI) were calculated. Stratified analysis was done; P < .05 was considered statistically significant. Of the total 1727 enrolled children, 1674 (97%) with complete results for at least one sputum/gastric aspirate sample were analyzed. The sensitivity of Xpert MTB/RIF was 68.5% in sputum and 53.6% in gastric aspirate while the specificity was 99% for both. The sensitivity compared to smear was 68.5% vs. 33.7% (P < .001) and 53.6% vs. 14.5%; (P < .001) in sputum and gastric aspirate, respectively. The sensitivity of Xpert MTB/RIF was 23.9% with decision to treat as reference standard. Xpert MTB/RIF positivity was significantly influenced by sex, age, nutritional status, chest X-ray abnormality, TB infection status, and symptoms suggestive of TB. Xpert MTB/RIF as an upfront test compared to smear improves diagnosis of pulmonary TB in children yet the sensitivity is suboptimal. Newer TB diagnostic tools with improved sensitivity is warranted in children.
We evaluated the performance of Xpert MTB/RIF in the diagnosis of pulmonary TB in children and explored the characteristics influencing Xpert MTB/RIF positivity. Sputum and or early morning gastric aspirate specimen was collected from children aged <15 years with symptoms suggestive of pulmonary TB. This was examined by smear (fluorescence microscopy), Xpert MTB/RIF, and culture (Mycobacteria growth indicator tube (MGIT)/Lowenstein–Jensen (LJ) medium). Diagnostic performance of Xpert MTB/RIF was evaluated using LJ and or MGIT culture positivity as the reference standard. Of the total 1727 enrolled children, 1674 (97%) with complete results for at least one sputum/gastric aspirate sample were analyzed. The sensitivity of Xpert MTB/RIF was 68.5% in sputum and 53.6% in gastric aspirate which was higher than smear and the specificity was 99%. The sensitivity of Xpert MTB/RIF was 23.9% with decision to treat for TB as reference standard. The Xpert MTB/RIF positivity was influenced by sex, age, nutritional status, chest X-ray abnormality, TB infection status, and symptoms suggestive of TB. Xpert MTB/RIF as an upfront test compared to smear improves the diagnosis of pulmonary TB in children yet the sensitivity is suboptimal. Newer TB diagnostic tools with improved sensitivity is warranted in children.

To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-low from HER2-positive breast cancer (BC). A total of 233 patients with pathologically confirmed invasive breast cancer admitted to our hospital between January 2018 and December 2022 were included in this retrospective analysis. Of these, 103 cases were diagnosed as HER2-positive and 130 cases were HER2 low-expression BC. The Synthetic Minority Oversampling Technique is employed to address the class imbalance problem. Patients were randomly split into a training set (163 cases) and a validation set (70 cases) in a 7:3 ratio. Radiomics features from DCE-MRI second-phase imaging were extracted. Z-score normalization was used to standardize the radiomics features, and Pearson\'s correlation coefficient and recursive feature elimination were used to explore the significant features. Prediction models were constructed using 6 machine learning algorithms: logistic regression, random forest, support vector machine, AdaBoost, decision tree, and auto-encoder. Receiver operating characteristic curves were constructed, and predictive models were evaluated according to the area under the curve (AUC), accuracy, sensitivity, and specificity. In the training set, the AUC, accuracy, sensitivity, and specificity of all models were 1.000. However, in the validation set, the auto-encoder model\'s AUC, accuracy, sensitivity, and specificity were 0.994, 0.976, 0.972, and 0.978, respectively. The remaining models\' AUC, accuracy, sensitivity, and specificity were 1.000. The DeLong test showed no statistically significant differences between the machine learning models in the training and validation sets (Z = 0, P = 1). Our study investigated the feasibility of using DCE-MRI-based radiomics features to predict HER2-low BC. Certain radiomics features showed associations with HER2-low BC and may have predictive value. Machine learning prediction models developed using these radiomics features could be beneficial for distinguishing between HER2-low and HER2-positive BC. These noninvasive preoperative models have the potential to assist in clinical decision-making for HER2-low breast cancer, thereby advancing personalized clinical precision.