Difference In Diagnostic Performance Research Articles

Diagnostic Performance of 18F-FDG PET/CT According to Delay After Treatment to Detect Subclinical Recurrence of Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) remains a malignancy with high rates of locoregional recurrence and poor prognosis for recurrent cases. Early detection of subclinical lesions is challenging but critical for effective patient management. Imaging surveillance after treatment, particularly 18F-FDG PET/CT, has shown promise in the diagnosis of HNSCC recurrence. The aim was to evaluate the diagnostic performance of 18F-FDG PET/CT according to delay after treatment in detecting subclinical recurrence (SCR) in HNSCC patients. Methods: In this retrospective study, all 18F-FDG PET/CT scans were performed at a single center. All adults with histologically proven HNSCC who were treated with curative intent between January 1, 2006, and December 31, 2021, were included. They had a normal clinical examination before each scan. Patients who underwent an intensive follow-up strategy after treatment had 18F-FDG PET/CT with an intravenous contrast agent at 3-6 mo and annually thereafter for 5 y. The primary endpoint was diagnostic performance (positive and negative predictive values, sensitivity, specificity, and accuracy). Results: In total, 2,566 18F-FDG PET/CT scans were performed among 852 patients, with an average of 3 scans per patient. The overall diagnostic performance measures were as follows: positive predictive value (88%), negative predictive value (98%), sensitivity (98%), specificity (89%), and accuracy (93%). There were no significant differences in diagnostic performance over time. The scans detected 126 cases of SCR (14.8%) and 118 cases of metachronous cancer (13.8%). The incidence of SCR decreased over time, with the highest detection rate in the first 2 y after treatment. Positive predictive value improved over time, reaching 90% for the digital Vision 600 system (third period) compared with 76% for the analog Gemini GXLi system (first period, P < 0.001). Multivariate analysis identified advanced stage, high body mass index, and initial PET/CT upstaging as predictive factors for detection of SCR. Conclusion: Our study demonstrates that 18F-FDG PET/CT has high diagnostic performance in detecting SCR during follow-up after treatment of HNSCC, especially in the first 2 y. Advanced tumor stage, initial PET/CT upstaging, and high body mass index were associated with a higher likelihood of SCR detection. The routine use of 18F-FDG PET/CT during follow-up seems justified for patients with HNSCC.

Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor

PurposeTo investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. MethodsTotally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. ResultsIn this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. ConclusionTime-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

Comparative analysis of computer-aided imaging collaboration: MRI versus CT for detection of knee joint injuries in athletes

ObjectiveThis study aims to compare the diagnostic efficacy of collaborative computer-aided (CAD) imaging diagnosis techniques in detecting knee joint injuries among athletes. The focus is on analyzing the differences in diagnostic performance between Magnetic Resonance Imaging (MRI) and Multilayer Spiral Computed Tomography (MSCT). The objective is to assess the performance of these two imaging modalities in diagnosing knee joint injuries. MethodsA total of 242 suspected knee joint injury patients were enrolled in the study. The patients underwent MRI and MSCT examinations using a self-developed Knee Joint Injury CAD system for auxiliary diagnosis. The Knee Joint Injury CAD system, employing the Monte Carlo and Markov chain algorithms, was utilized to analyze MRI and CT images, assisting physicians in fracture diagnosis. False-positive annotations were removed through discussions and consultations among physicians to obtain independent detection results. Arthroscopy was utilized as the gold standard. The diagnostic results for MRI and CT assisted by the CAD software were recorded, and metrics such as diagnostic sensitivity, specificity, and overall accuracy were calculated. Furthermore, the accuracy of diagnosing bone and ligament injuries was analyzed. ResultsThe MRI diagnostic sensitivity of the collaborative Knee Joint Injury CAD system was 94.75%, with a specificity of 97.42% and an accuracy of 96.01%. In contrast, the diagnostic sensitivity of the collaborative Knee Joint Injury CAD system for MSCT was 89.36%, with a specificity of 85.71% and an accuracy of 89.86%. A comparison between the two detection methods reveals that MRI outperforms CT in terms of imaging quality but requires a longer examination time. The average imaging quality score for MRI was 6.89 ± 0.27, with an average examination time of 28.71 ± 7.64 minutes. On the other hand, the average imaging quality score for CT was 6.71 ± 0.42, with an average examination time of 9.95 ± 2.42 min. ConclusionIn diagnosing knee joint injuries, the collaborative Knee Joint Injury CAD system with MRI assistance in computer-aided medical diagnosis demonstrates higher diagnostic sensitivity, specificity, and accuracy compared to the collaborative Knee Joint Injury CAD system with MSCT. Therefore, the clinical application of a collaborative Knee Joint Injury CAD system with MRI exhibits superior diagnostic performance in knee joint injuries, especially in detecting ligament and soft tissue injuries. However, it is worth noting that a collaborative Knee Joint Injury CAD system with MRI has a higher imaging quality score and longer examination time. The trade-off between these factors needs to be considered in clinical practice, and the choice of imaging technology should be based on specific circumstances.

Intraoperative in vivo confocal endomicroscopy of the glioma margin: performance assessment of image interpretation by neurosurgeon users.

Confocal laser endomicroscopy (CLE) is an intraoperative real-time cellular resolution imaging technology that images brain tumor histoarchitecture. Previously, we demonstrated that CLE images may be interpreted by neuropathologists to determine the presence of tumor infiltration at glioma margins. In this study, we assessed neurosurgeons' ability to interpret CLE images from glioma margins and compared their assessments to those of neuropathologists. In vivo CLE images acquired at the glioma margins that were previously reviewed by CLE-experienced neuropathologists were interpreted by four CLE-experienced neurosurgeons. A numerical scoring system from 0 to 5 and a dichotomous scoring system based on pathological features were used. Scores from assessments of hematoxylin and eosin (H&E)-stained sections and CLE images by neuropathologists from a previous study were used for comparison. Neurosurgeons' scores were compared to the H&E findings. The inter-rater agreement and diagnostic performance based on neurosurgeons' scores were calculated. The concordance between dichotomous and numerical scores was determined. In all, 4275 images from 56 glioma margin regions of interest (ROIs) were included in the analysis. With the numerical scoring system, the inter-rater agreement for neurosurgeons interpreting CLE images was moderate for all ROIs (mean agreement, 61%), which was significantly better than the inter-rater agreement for the neuropathologists (mean agreement, 48%) (p < 0.01). The inter-rater agreement for neurosurgeons using the dichotomous scoring system was 83%. The concordance between the numerical and dichotomous scoring systems was 93%. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 78%, 32%, 62%, and 50%, respectively, using the numerical scoring system and 80%, 27%, 61%, and 48%, respectively, using the dichotomous scoring system. No statistically significant differences in diagnostic performance were found between the neurosurgeons and neuropathologists. Neurosurgeons' performance in interpreting CLE images was comparable to that of neuropathologists. These results suggest that CLE could be used as an intraoperative guidance tool with neurosurgeons interpreting the images with or without assistance of the neuropathologists. The dichotomous scoring system is robust yet simple and may streamline rapid, simultaneous interpretation of CLE images during imaging.

Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos

IntroductionOur objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology. MethodsData was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed. ResultsThe sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70±14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%). ConclusionFor expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.

Screening for Primary Aldosteronism by Mass Spectrometry Versus Immunoassay Measurements of Aldosterone: A Prospective Within-Patient Study.

Measurements of aldosterone by mass spectrometry are more accurate and less prone to interferences than immunoassay measurements, and may produce a more accurate aldosterone:renin ratio (ARR) when screening for primary aldosteronism (PA). Differences in diagnostic performance of the ARR using mass spectrometry vs immunoassay measurements of aldosterone were examined in 710 patients screened for PA. PA was confirmed in 153 patients and excluded in 451 others. Disease classifications were not achieved in 106 patients. Areas under receiver-operating characteristic curves (AUROC) and other measures were used to compare diagnostic performance. Mass spectrometry-based measurements yielded lower plasma aldosterone concentrations than immunoassay measurements. For the ARR based on immunoassay measurements of aldosterone, AUROCs were slightly lower (P = 0.018) than those using mass spectrometry measurements (0.895 vs 0.906). The cutoff for the ARR to reach a sensitivity of 95% was 30 and 21.5 pmol/mU by respective immunoassay and mass spectrometry-based measurements, which corresponded to specificities of 57% for both. With data restricted to patients with unilateral PA, diagnostic sensitivities of 94% with specificities >81% could be achieved at cutoffs of 61 and 52 pmol/mU for respective immunoassay and mass spectrometry measurements. Mass spectrometry-based measurements of aldosterone for the ARR provide no clear diagnostic advantage over immunoassay-based measurements. Both approaches offer limited diagnostic accuracy for the ARR as a screening test. One solution is to employ the higher cutoffs to triage patients likely to have unilateral PA for further tests and possible adrenalectomy, while using the lower cutoffs to identify others for targeted medical therapy.German Clinical Trials Register ID: DRKS00017084.

Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.

Accurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases. We collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature. A total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963-1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967-1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model. This ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.

Learning imaging in axial spondyloarthritis: more than just a matter of experience

ObjectiveReliable interpretation of imaging findings is essential for the diagnosis of axial spondyloarthritis (axSpA) and requires a high level of experience. We investigated experience-dependent differences in diagnostic accuracies using X-ray...

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Sex Differences in Prehospital Identification of Large Vessel Occlusion in Patients With Suspected Stroke.

Differences in clinical presentation of acute ischemic stroke between men and women may affect prehospital identification of anterior circulation large vessel occlusion (aLVO). We assessed sex differences in diagnostic performance of 8 prehospital scales to detect aLVO. We analyzed pooled individual patient data from 2 prospective cohort studies (LPSS [Leiden Prehospital Stroke Study] and PRESTO [Prehospital Triage of Patients With Suspected Stroke Study]) conducted in the Netherlands between 2018 and 2019, including consecutive patients ≥18 years suspected of acute stroke who presented within 6 hours after symptom onset. Ambulance paramedics assessed clinical items from 8 prehospital aLVO detection scales: Los Angeles Motor Scale, Rapid Arterial Occlusion Evaluation, Cincinnati Stroke Triage Assessment Tool, Cincinnati Prehospital Stroke Scale, Prehospital Acute Stroke Severity, gaze-face-arm-speech-time, Conveniently Grasped Field Assessment Stroke Triage, and Face-Arm-Speech-Time Plus Severe Arm or Leg Motor Deficit. We assessed the diagnostic performance of these scales for identifying aLVO at prespecified cut points for men and women. Of 2358 patients with suspected stroke (median age, 73 years; 47% women), 231 (10%) had aLVO (100/1114 [9%] women and 131/1244 [11%] men). The area under the curve of the scales ranged from 0.70 (95% CI, 0.65-0.75) to 0.77 (95% CI, 0.73-0.82) in women versus 0.69 (95% CI, 0.64-0.73) to 0.75 (95% CI, 0.71-0.79) in men. Positive predictive values ranged from 0.23 (95% CI, 0.20-0.27) to 0.29 (95% CI, 0.26-0.31) in women versus 0.29 (95% CI, 0.24-0.33) to 0.37 (95% CI, 0.32-0.43) in men. Negative predictive values were similar (0.95 [95% CI, 0.94-0.96] to 0.98 [95% CI, 0.97-0.98] in women versus 0.94 [95% CI, 0.93-0.95] to 0.96 [95% CI, 0.94-0.97] in men). Sensitivity of the scales was slightly higher in women than in men (0.53 [95% CI, 0.43-0.63] to 0.76 [95% CI, 0.68-0.84] versus 0.49 [95% CI, 0.40-0.57] to 0.63 [95% CI, 0.55-0.73]), whereas specificity was lower (0.79 [95% CI, 0.76-0.81] to 0.87 [95% CI, 0.84-0.89] versus 0.82 [95% CI, 0.79-0.84] to 0.90 [95% CI, 0.88-0.91]). Rapid arterial occlusion evaluation showed the highest positive predictive values in both sexes (0.29 in women and 0.37 in men), reflecting the different event rates. aLVO scales show similar diagnostic performance in both sexes. The rapid arterial occlusion evaluation scale may help optimize prehospital transport decision-making in men as well as in women with suspected stroke.

Synovial Fluid C-reactive Protein Clinical Decision Limit and Diagnostic Accuracy for Periprosthetic Joint Infection.

Introduction C-reactive protein (CRP) has long served as a prototypical biomarker for periprosthetic joint infection (PJI). Recently, synovial fluid (SF)-CRP has garnered interest as a diagnostic tool, with several studies demonstrating its diagnostic superiority over serum CRP for the diagnosis of PJI. Although previous studies have identified diagnostic thresholds for SF-CRP, they have been limited in scope and employed various CRP assays without formal validation for PJI diagnosis. This study aimed to conduct a formal single clinical laboratory validation to determine the optimal clinical decision limit of SF-CRP for the diagnosis of PJI. Methods A retrospective analysis of prospectively collected data was performed using receiver operating characteristic (ROC) and area under the curve (AUC) analyses. Synovial fluid samples from hip and knee arthroplasties, received from over 2,600 institutions,underwent clinical testing for PJI at a single clinical laboratory (CD Laboratories, Zimmer Biomet, Towson, MD) between 2017 and 2022. Samples were assayed for SF-CRP, alpha-defensin, white blood cell count, neutrophil percentage, and microbiological culture. After applying selection criteria, the samples were classified with the 2018 ICM PJI scoring system as "infected," "not infected," or "inconclusive." Data were divided into training and validation sets. The Youden Index was employed to optimize the clinical decision limit. Results A total of 96,061 samples formed the training (n = 67,242) and validation (n = 28,819) datasets. Analysis of the biomarker median values, culture positivity, anatomic distribution, and days from aspiration to testing revealed nearly identical specimen characteristics in both the training set and validation set. SF-CRP demonstrated an AUC of 0.929 (95% confidence interval (CI): 0.926-0.932) in the training set, with an optimal SF-CRP clinical decision limit for PJI diagnosis of 4.45 mg/L. Applying this cutoff to the validation dataset yielded a sensitivity of 86.1% (95% CI: 85.0-87.1%) and specificity of 87.1% (95% CI: 86.7-87.5%). No statistically significant difference in diagnostic performance was observed between the validation and training sets. Conclusion This study represents the largest single clinical laboratory evaluation of an SF-CRP assay for PJI diagnosis. The optimal CRP cutoff (4.45 mg/L) for PJI, which yielded a sensitivity of 86.1% and a specificity of 87.1%, is specific to the assay methodology and laboratory performing the assay. We propose that an SF-CRP test with a laboratory-validated optimal clinical decision limit for PJI may be preferable, in a clinical diagnostic setting, to serum CRP tests that do not have laboratory-validated clinical decision limits for PJI.

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography

PurposeTo evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. MethodsThis retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). ResultsTwenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. ConclusionRadiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.

Head-to-head comparison of cone-beam breast computed tomography and mammography in the diagnosis of primary breast cancer: A systematic review and meta-analysis

IntroductionTo compare the diagnostic performance of cone-beam breast computed tomography (CBBCT) and mammography (MG) in primary breast cancer. MethodsPubMed, Embase, Web of Science, China National Knowledge Infrastructure, WanFang DATA, and China Science and Technology Journal databases were searched comprehensively from inception to March 2023. Sensitivity and specificity were calculated using bivariate random-effects models, and a summary receiver operating characteristic (SROC) curve was constructed. Bivariate I2 statistics and meta-regression analyses were also performed. The differences in diagnostic performance between CBBCT and MG were analysed using Z-test statistics. Clinical utility was explored using Fagan’s nomogram, and quality assessment was conducted utilising the Quality Assessment of Diagnostic Accuracy Studies-2 checklist. ResultsThe summary sensitivity and specificity for CBBCT in diagnosing primary breast cancer were 0.92 (95 % CI: 0.87–0.94) and 0.79 (95 % CI: 0.71–0.85), respectively, and the area under the curve (AUC) of the SROC was 0.93 (95 % CI: 0.90–0.95). For MG, the summary sensitivity and specificity were 0.77 (95 % CI: 0.69–0.83) and 0.75 (95 % CI: 0.66–0.82), respectively, with an AUC of 0.83 (95 % CI: 0.80–0.86). The Z-test revealed that the summary sensitivity of CBBCT was significantly higher than that of MG (P < 0.001). Additionally, the summary AUC of CBBCT was significantly higher than that of MG (P < 0.001). ConclusionThe diagnostic performance of CBBCT for primary breast cancer was better than that of MG. However, the results of both the CBBCT and MG are based on studies with small sample sizes. Further studies with larger sample sizes and more comprehensive designs are required to address this issue.

An economically efficient strategy for diagnosing atypia of undetermined significance or follicular lesion of undetermined significance thyroid nodules with ultrasound-based risk stratification systems and BRAFV600E testing.

The management of thyroid nodules classified as atypia of undetermined significance or follicular lesion of undetermined significance (AUS/FLUS) has been a subject of ongoing debate. Therefore, the aim of this study was to investigate a cost-effective approach for managing these nodules by combining BRAFV600E mutation analysis with the guidelines provided by the American Thyroid Association (ATA) or the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS). This study included 762 AUS/FLUS nodules in 551 patients with a postoperative pathology. A preoperative BRAFV600E gene test and an evaluation using the ATA guidelines and ACR-TIRADS were performed. Two combined diagnostic approaches were employed: In method 1, all nodules underwent BRAFV600E gene testing, and nodules testing positive for BRAFV600E or for risk stratification systems (RSSs) were diagnosed as malignant, while those with negative results in both tests were considered benign. In method 2 (modified combination method), nodules were reclassified into low-risk (category 2 and 3 in the ATA guidelines and ACR-TIRADS), medium-risk (category 4), and high-risk (category 5) groups based on the malignancy rate of the RSSs. BRAFV600E gene testing was applied only with the medium-risk group. Nodules with positive BRAFV600E mutation were upgraded to the high-risk group, while negative cases remained in the medium-risk group. Both malignancy rates and positive BRAFV600E mutation rates increased with the increase in RSS category (P<0.001). The combination of ACR with BRAFV600E gene testing significantly improved the area under the curve (AUC) compared to the use of ACR or BRAFV600E alone (the AUCs for ACR combined with BRAFV600E, modified ACR combined with BRAFV600E, ACR alone, and BRAFV600E alone were 0.875, 0.878, 0.832, and 0.839, respectively; P<0.05 for both combinations vs. ACR or BRAFV600E alone). Similarly, ATA combined with BRAFV600E showed significant improvements in AUC compared to ATA alone (the AUCs for ATA combined with BRAFV600E, modified ATA combined with BRAFV600E, and ATA alone were 0.851, 0.846, 0.809, respectively; P<0.001 for both combination methods vs. ATA alone), but there was no significant difference observed compared to using BRAFV600E alone (P=0.450 and P=0.680 for both combination methods vs. BRAFV600E). Notably, the AUC of ACR combined with BRAFV600E was greater than that of ATA combined with BRAFV600E (P=0.047 and P=0.007 for both combination methods, respectively). There were no significant differences in diagnostic performance between the two combination approaches (P=0.428 for ACR combined with BRAFV600E and P=0.314 for ATA combined with BRAFV600E). Performing BRAFV600E gene testing only on the medium-risk groups (modified combination method) significantly reduced the rate of BRAFV600E gene testing (P<0.001) without increasing the false-negative rate (P=0.818 and P=0.394 for ACR and ATA, respectively). Incorporating the BRAFV600E gene test exclusively for nodules in the medium-risk group significantly improved diagnostic efficacy, reduced the utilization of gene tests, and maintained a consistent false-negative rate.

Diagnostic performance of generative artificial intelligences for a series of complex case reports

Background Diagnostic performance of generative artificial intelligences (AIs) using large language models (LLMs) across comprehensive medical specialties is still unknown. Objective We aimed to evaluate the diagnostic performance of generative AIs using LLMs in complex case series across comprehensive medical fields. Methods We analyzed published case reports from the American Journal of Case Reports from January 2022 to March 2023. We excluded pediatric cases and those primarily focused on management. We utilized three generative AIs to generate the top 10 differential-diagnosis (DDx) lists from case descriptions: the fourth-generation chat generative pre-trained transformer (ChatGPT-4), Google Gemini (previously Bard), and LLM Meta AI 2 (LLaMA2) chatbot. Two independent physicians assessed the inclusion of the final diagnosis in the lists generated by the AIs. Results Out of 557 consecutive case reports, 392 were included. The inclusion rates of the final diagnosis within top 10 DDx lists were 86.7% (340/392) for ChatGPT-4, 68.6% (269/392) for Google Gemini, and 54.6% (214/392) for LLaMA2 chatbot. The top diagnoses matched the final diagnoses in 54.6% (214/392) for ChatGPT-4, 31.4% (123/392) for Google Gemini, and 23.0% (90/392) for LLaMA2 chatbot. ChatGPT-4 showed higher diagnostic accuracy than Google Gemini ( P &lt; 0.001) and LLaMA2 chatbot ( P &lt; 0.001). Additionally, Google Gemini outperformed LLaMA2 chatbot within the top 10 DDx lists ( P &lt; 0.001) and as the top diagnosis ( P = 0.010). Conclusions This study demonstrated the diagnostic performance of generative AIs including ChatGPT-4, Google Gemini, and LLaMA2 chatbot. ChatGPT-4 exhibited higher diagnostic accuracy than the other platforms. These findings suggest the importance of understanding the differences in diagnostic performance among generative AIs, especially in complex case series across comprehensive medical fields, like general medicine.

Diagnostic Value of American Thyroid Association Guidelines,American College of Radiology Thyroid Imaging Reporting and Data System,and Chinese Thyroid Imaging Reporting and Data System Alone and Combined With BRAFV600E Mutation in Atypia of Undetermined Significance/Follicular Lesion of Undetermined Significance

Objective To explore the diagnostic efficacy of American Thyroid Association(ATA)guidelines,American College of Radiology Thyroid Imaging Report and Data System(ACR-TIRADS),and Chinese Thyroid Imaging Reporting and Data System(C-TIRADS)alone and combined with BRAFV600E mutation in atypia of undetermined significance/follicular lesion of undetermined significance(AUS/FLUS).Methods A total of 138 patients who underwent ultrasound-guided fine needle aspiration(FNA)in the Chinese PLA General Hospital from January 2020 to May 2023 were selected.The clinicopathological and ultrasound characteristics were retrospectively analyzed for each nodule.Each nodule underwent preoperative BRAFV600E mutation testing and was diagnosed according to the ATA guidelines,ACR-TIRADS,and C-TIRADS.The diagnostic efficacy of ATA guidelines,ACR-TIRADS,and C-TIRADS alone and combined with BRAFV600E mutation was assessed based on the results of histopathological diagnosis.Results The 138 AUS/FLUS thyroid nodules included 45(32.6%)benign ones and 93(67.4%)malignant ones.The patient age(t=1.444,P=0.151),gender(χ2=0.259,P=0.611),and location of nodules(χ2=2.055,P=0.358)had no statistical significance for the differentiation between benign and malignant nodules,while nodule size(Z=2.500,P=0.012),echo(χ2=14.693,P<0.001),composition(χ2=17.075,P<0.001),aspect ratio ≥1(χ2=9.477,P=0.002),and microcalcification(χ2=6.892,P=0.009)were of significance for the differentiation.When applied alone,BRAFV600E mutation showed high specificity(95.56%)and positive predictive value(95.65%).Among the three ultrasound grading systems,ACR-TIRADS had the highest sensitivity(χ2=37.923,P<0.001;χ2=40.462,P<0.001)and accuracy(χ2=81.595,P<0.001;χ2=76.912,P<0.001),while C-TIRADS had the highest specificity(χ2=11.746,P<0.001;χ2=21.235,P<0.001).However,the three systems showed no statistically significant difference in the diagnostic efficiency when applied alone(Z=1.177,P=0.239;Z=0.213,P=0.831;Z=1.016,P=0.310).The combination of BRAFV600E mutation with ACR-TIRADS or C-TIRADS improved the diagnostic efficacy of BRAFV600E mutation in distinguishing the benign and malignant AUS/FLUS nodules(Z=2.107,P=0.035;Z=2.752,P=0.006).The combination of ATA guidelines with BRAFV600E mutation increased the diagnostic accuracy of BRAFV600E mutation(χ2=20.679,P<0.001),while it had no statistically significant difference in distinguishing the benign and malignant AUS/FLUS nodules(Z=1.321,P=0.186).The combination of ATA guidelines,ACR-TIRADS,or C-TIRADS with BRAFV600E mutation improved the diagnostic efficacy of ultrasound grading systems for AUS/FLUS nodules(Z=2.770,P=0.006;Z=2.770,P=0.006;Z=2.890,P=0.004).Specifically,ACR-TIRADS combined with BRAFV600E mutation showed the highest sensitivity(χ2=4.712,P=0.030;χ2=4.712,P=0.030),while C-TIRADS combined with BRAFV600E mutation showed the highest accuracy(χ2=77.627,P<0.001;χ2=85.827,P<0.001).However,there were no statistically significant differences in diagnostic performance between the combinations(Z=1.276,P=0.202;Z=0.808,P=0.419;Z=1.615,P=0.106).Conclusion ATA guidelines,ACR-TIRADS,and C-TIRADS combined with BRAFV600E mutation can improve the diagnostic efficacy of BRAFV600E mutation or ultrasound grading system alone in AUS/FLUS nodules,which can facilitate the further management and treatment of such patients.

Is preoperative pancreatic juice cytology useful for determining therapeutic strategies for patients with intraductal papillary mucinous neoplasm of the pancreas?

We compared the results of preoperative pancreatic juice cytology (PJC) and final pathological diagnosis after resection in patients who underwent resection of intraductal papillary mucinous neoplasm (IPMN) of the pancreas to determine whether preoperative PJC can help determine therapeutic strategies. Of 1130 patients who underwent surgical resection IPMN at 11 Japanese tertiary institutions, the study included 852 patients who underwent preoperative PJC guided by endoscopic retrograde cholangiopancreatography (ERCP). The accuracy of preoperative PJC for differentiation between cancerous and noncancerous lesions were 55% for IPMN overall; 59% for the branch duct type; 49% for the main pancreatic duct type; 53% for the mixed type, respectively. On classifying IPMN according to the diameters of the mural nodule (MN) and main pancreatic duct (MPD), the corresponding values for diagnostic performance were 40% for type 1 (MN ≥5 mm and MPD ≥ 10 mm); 46% for type 2 (MN ≥5 mm and MPD < 10 mm); 61% for type 3 (MN < 5 mm and MPD ≥ 10 mm); 72% for type 4 (MN < 5 mm and MPD < 10 mm), respectively. PJC in IPMN is not a recommended examination because of its low overall sensitivity and no significant difference in diagnostic performance by type, location, or subclassification. Although the sensitivity is low, the positive predictive value is high, so we suggest that pancreatic juice cytology be performed only in cases where the patient is not sure about surgery.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PurposeThe purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. Materials and methodsSixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29–78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. ResultsThe 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73–0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66–0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74–0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74–0.93) (P = 0.60). ConclusionBoth whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

Axial T1-weighted imaging of the lumbar spine: a redundancy or an asset?

To determine the diagnostic value of axial T1-weighted imaging for patients suffering from lower back pain. In this retrospective study, 100 consecutive lumbar spine MRIs obtained in patients with chronic low back pain were reviewed in two sessions: First, readers viewed core sequences (sagittal T1-weighted, STIR and T2-weighted, and axial T2-weighted) with axial T1-weighted sequences, and second, readers viewed cores sequences alone. Readers recorded the presence of disc degeneration, nerve root compromise, facet joint arthritis, and stenosis at each lumbar spine level as well as the presence of lipoma of filum terminale (LFT), spondylolisthesis, transitional vertebrae, and fractures. The McNemar, Wilcoxon signed-rank, and student T tests were utilized. For 100 studies, 5 spine levels were evaluated (L1-L2 through L5-S1). There were cases of disc disease (444/500 bulges, 56/500 herniations), nerve root compromise (1/500 nerve enlargement, 36/500 contact only, 20/500 displacement or compression), facet arthritis (438/500), stenosis (58/500 central canal, 64/500 lateral recess, 137/500 neuroforaminal), 6/100 LFTs, and other abnormalities (58/500 spondylolisthesis, 10/100 transitional vertebrae, 10/500 fracture/spondylolysis). There was no difference in diagnostic performance between the interpretation sessions (with and without axial T1-weighted imaging) at any level (p > 0.05), although four small additional LFTs were identified with axial T1-weighted imaging availability. There was no clinically significant difference in the interpretation of lumbar spine MRI viewed with and without axial T1-weighted imaging, suggesting that the axial T1-weighted sequence does not add diagnostic value to routine lumbar spine MRI.

Diagnostic performance of contrast-enhanced ultrasound in breast lesions: what diagnostic models could be used for lesions of different sizes?

Previous studies show the size of lesions could affect the diagnostic accuracy of contrast-enhanced ultrasound (CEUS). It is unclear whether CEUS has good diagnostic performance for lesions ≤2.0 and ≤1.0 cm. It is beneficial for the early diagnosis to explore the application of CEUS in breast lesions of different sizes. This study aims to analyze the diagnostic performance of CEUS and explore diagnostic models better suited to breast lesions of different sizes. A total of 1,059 lesions (656 benign and 403 malignant) examined by ultrasound and CEUS with definite pathological results were included in this retrospective study and divided into training (n=847) and validation (n=212) sets. All lesions were divided into three groups according to size. Diagnostic models (M0: all lesions; M1: ≤1.0 cm, M2: >1.0-2.0 cm, and M3: >2.0 cm) were developed through logistic regression analyses of CEUS features from the training set. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) and validated in the validation set. The median age of patients was 45±11 years (range, 18-80 years). The AUC values of M0 combined with the Breast Imaging Reporting and Data System (BI-RADS) in the training and validation sets were 0.921 and 0.922, respectively (P=0.893). The AUC values of M0 combined with BI-RADS in the three groups were 0.844, 0.936 and 0.928 respectively. M0 was less effective in diagnosing lesions ≤1.0 cm (0.844 vs. 0.921, P=0.029). The AUC of M1 combined with BI-RADS for lesions ≤1.0 cm was higher than that of M0 (0.893 vs. 0.844, P=0.047), and M2 and M3 had no statistical difference in diagnostic performance when compared with M0 (P=0.243; P=0.246). The diagnostic performance of CEUS was closely related to lesion size. Establishing a new diagnostic model for lesions ≤1.0 cm can improve the CEUS diagnostic performance for breast lesions ≤1.0 cm.