Unnecessary Biopsies Research Articles

Pediatric Diffuse Lung Disease in Infants: Imaging Findings and Histopathologic Correlation.

Childhood interstitial lung disease (chILD) encompasses a diverse group of genetic, infectious, and inflammatory conditions affecting infants and children. The recognition and understanding of these entities have highlighted the necessity for more accurate classification. This group of rare heterogeneous diseases comprises more than 200 different conditions and has a combined estimated prevalence of less than one patient per 100 000 children. Hence, a systematic diagnostic approach is crucial. This article describes a diagnostic approach for pediatric diffuse lung diseases in infancy, including an analysis of clinical presentations and imaging and histologic features to effectively distinguish among various chILD entities. Although they often have overlapping and nonspecific radiologic features, some chILD entities may exhibit typical imaging findings, resulting in a CT diagnosis or aiding in narrowing the differential diagnosis, thus guiding the clinician to the appropriate genetic tests, potentially limiting unnecessary biopsies. This approach aims to enhance the understanding and diagnosis of chILD in infants, thereby facilitating improved patient care.

Advancing prostate cancer detection: High-accuracy urine test distinguishes between high and low-risk cancers

Prostate cancer remains one of the leading causes of cancer-related deaths among men. The standard method for early detection involves measuring Prostate-specific antigen (PSA) levels in the blood, with elevated PSA levels prompting further diagnostic procedures, such as a prostate biopsy. While effective, biopsies can be painful and carry risks of complications such as fever and urinary tract infections. To reduce unnecessary biopsies, researchers have sought alternative diagnostic methods. In recent years, a urine test was developed for early-stage prostate cancer detection. However, this test struggled to differentiate between aggressive and slow-growing cancers, the latter of which often require minimal treatment and are managed through active surveillance. To address this limitation, scientists at Wunderkind University have significantly enhanced the urine test. By analysing the genomes of thousands of prostate cancer patients, they identified a panel of 16 genes detectable in urine that can effectively distinguish between high-risk and low-risk cancers. When applied to individuals with elevated PSA levels, this improved test demonstrated an impressive 97% accuracy in identifying advanced prostate cancer. This advancement holds great promise for reducing unnecessary biopsies and improving patient outcomes by accurately identifying those needing aggressive treatment versus those suitable for observation. This review paper will explore the development, validation, and clinical implications of this novel urine test, highlighting its potential to transform prostate cancer diagnostics and patient management.

Case Report: All that glitters is not cancer; perihepatic hibernoma with fluctuating FDG uptake on PET/CT

Hibernomas are rare brown fat tumors that garnered attention in the literature with the increasing use of [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography ([18F] FDG PET/CT) for the staging workup and follow-up of solid malignancies. Despite being benign tumors, they exhibit high metabolic activity due to their thermogenic nature, leading to significant radiotracer uptake on functional imaging. This can pose a challenge in differentiating them from the malignant lesions, especially the fat-containing malignancies such as liposarcoma. Hibernomas are typically found in the thigh, shoulder, back, and neck. Here, we present a unique case of Hibernoma in a patient undergoing PET/CT for melanoma follow-up in an unusual perihepatic location. To the best of the authors’ knowledge, this represents the first reported case of a perihepatic hibernoma in the literature. The report also offers a literature review on hibernomas, including the influence of ambient temperature on their metabolism, diagnostic challenges, management strategies, and reports of hibernomas detected on functional imaging with a range of radiotracers. These observations could serve as a valuable clue in identifying hibernomas, potentially aiding in avoiding unnecessary biopsies or resections.

Using a novel PSMA-PET and PSA-based model to enhance the diagnostic accuracy for clinically significant prostate cancer and avoid unnecessary biopsy in men with PI-RADS ≤ 3 MRI.

The diagnostic evaluation of men with suspected prostate cancer (PCa) yet inconclusive MRI (PI-RADS ≤ 3) presents a common clinical challenge. [68Ga]Ga-labelled prostate-specific membrane antigen ([68Ga]Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has shown promise in identifying clinically significant PCa (csPCa). We aim to establish a diagnostic model incorporating PSMA-PET to enhance the diagnostic process of csPCa in PI-RADS ≤ 3 men. This study retrospective included 151 men with clinical suspicion of PCa and PI-RADS ≤ 3 MRI. All men underwent [68Ga]Ga-PSMA PET/CT scans and ultrasound/MRI/PET fusion-guided biopsies. csPCa was defined as Grade Group ≥ 2. PRIMARY-scores from PSMA-PET scans were evaluated. A diagnostic model incorporating PSMA-PET and prostate-specific antigen (PSA)-derived parameters was developed. The discriminative performance and clinical utility were compared with conventional methods. Internal validation was conducted using a fivefold cross-validation with 1000 iterations. In this PI-RADS ≤ 3 cohort, areas-under-the-curve (AUCs) for detecting csPCa were 0.796 (95%CI, 0.738-0.853), 0.851 (95%CI, 0.783-0.918) and 0.806 (95%CI, 0.742-0.870) for PRIMARY-score, SUVmax and routine clinical PSMA-PET assessment, respectively. The diagnostic model comprising PRIMARY-score, SUVmax and serum free PSA/total PSA (fPSA/tPSA) achieved a significantly higher AUC of 0.906 (95%CI, 0.851-0.961) compared to strategies based on PRIMARY-score or SUVmax (P < 0.05) and markedly superior to conventional strategies typically based on PSA density (P < 0.001). The average fivefold cross-validated AUC with 1000 iterations was 0.878 (95%CI, 0.820-0.954). Theoretically, using a threshold of 21.6%, the model could have prevented 78% of unnecessary biopsies while missing only 7.8% of csPCa cases in this cohort. A novel diagnostic model incorporating PSMA-PET derived metrics-PRIMARY-score and SUVmax-along with serum fPSA/tPSA, has been developed and validated. The integrated model may assist clinical decision-making with enhanced diagnostic accuracy over the individual conventional metrics. It has great potential to reduce unnecessary biopsies for men with PI-RADS ≤ 3 MRI results and warrants further prospective and external evaluations.

Prostate cancer screening: Is it time for a new approach? A review article.

Prostate cancer screening has presented a challenge to clinicians. Although the implementation of screening tests such as prostate-specific antigen (PSA) and digital rectal exam (DRE) has had a significant impact on prostate-cancer-specific mortality, these traditional screening tests have a relatively poor positive predictive value of clinically significant prostate cancer (CSPC), leading to unnecessary biopsies and treatment with a host of potential complications. Fortunately, much research has been done to optimize prostate cancer screening. This includes the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, which underwent a secondary analysis to identify an association between PSA level and CSPC, and the IP1-PROSTAGRAM Tri.

Genetic testing, a challenge to kidney biopsy? A case report

The field of genetic testing has experienced significant growth in medical practice since 1956, when the first genetic analysis was introduced. Persistent proteinuria has long been considered a strong risk factor for the progression of chronic renal failure, though, paradoxically, it can also be a benign process, as seen in individuals with mutations in the cubilin (CUBN) protein, specifically the C-terminal. CUBN is a peripheral protein that plays a crucial role in the receptor-mediated endocytotic reabsorption of albumin in the proximal tubule. In the past, there have been misinterpretations of CUBN variants with isolated proteinuria as glomerular injury, leading to unnecessary kidney biopsies and ineffective treatments. This paper discusses two siblings with a homozygous variant of (p.Tyr3018Ser) in the C-terminal of the CUBN protein, inherited from both heterozygous carrier parents. This case presents an opportunity to question our typical approach to proteinuria in an effort to avoid unnecessary kidney biopsies and the subsequent side effects of treatments, particularly for those with proteinuria.

Assessing the feasibility of ChatGPT-4o and Claude 3-Opus in thyroid nodule classification based on ultrasound images.

Large language models (LLMs) are pivotal in artificial intelligence, demonstrating advanced capabilities in natural language understanding and multimodal interactions, with significant potential in medical applications. This study explores the feasibility and efficacy of LLMs, specifically ChatGPT-4o and Claude 3-Opus, in classifying thyroid nodules using ultrasound images. This study included 112 patients with a total of 116 thyroid nodules, comprising 75 benign and 41 malignant cases. Ultrasound images of these nodules were analyzed using ChatGPT-4o and Claude 3-Opus to diagnose the benign or malignant nature of the nodules. An independent evaluation by a junior radiologist was also conducted. Diagnostic performance was assessed using Cohen's Kappa and receiver operating characteristic (ROC) curve analysis, referencing pathological diagnoses. ChatGPT-4o demonstrated poor agreement with pathological results (Kappa = 0.116), while Claude 3-Opus showed even lower agreement (Kappa = 0.034). The junior radiologist exhibited moderate agreement (Kappa = 0.450). ChatGPT-4o achieved an area under the ROC curve (AUC) of 57.0% (95% CI: 48.6-65.5%), slightly outperforming Claude 3-Opus (AUC of 52.0%, 95% CI: 43.2-60.9%). In contrast, the junior radiologist achieved a significantly higher AUC of 72.4% (95% CI: 63.7-81.1%). The unnecessary biopsy rates were 41.4% for ChatGPT-4o, 43.1% for Claude 3-Opus, and 12.1% for the junior radiologist. While LLMs such as ChatGPT-4o and Claude 3-Opus show promise for future applications in medical imaging, their current use in clinical diagnostics should be approached cautiously due to their limited accuracy.

Ultrasound strain elastography to improve diagnostic performance of breast lesions by reclassifying BI-RADS 3 and 4a lesions: a multicenter diagnostic study.

To investigate the added value of strain elastography (SE) by recategorizing ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a lesions. A total of 4371 patients underwent US and SE with BI-RADS 2-5 categories solid breast lesions were included from thirty-two hospitals. We evaluated the elastographic images according to elasticity scores (ES) and strain ratios (SR). Three combined methods (BI-RADS+ES, BI-RADS+SR, BI-RADS+ES+SR) and two reclassified methods were used (method one: upgrading BI-RADS 3 and downgrading BI-RADS 4a, method two: downgrading BI-RADS 4a alone). The diagnostic performance and the potential reduction of unnecessary biopsies were evaluated. Combining BI-RADS with SE had a higher area under the curve (AUC) than BI-RADS alone (0.822-0.898 vs. 0.794, P<0.01). For reclassified method one, the sensitivity, specificity, and accuracy were 99.36%, 66.70%, 78.36% for BI-RADS+ES and 98.01%, 66.45%, 77.72% for BI-RADS+SR, and 99.42%, 66.70%, 78.38% for BI-RADS+ES+SR, respectively. For reclassified method two, the sensitivity, specificity, and accuracy were 99.17%, 70.72%, 80.87% for BI-RADS+ES and 97.76%, 81.75%, 87.46% for BI-RADS+SR, and 99.23%, 69.83%, 80.32% for BI-RADS+ES+SR, respectively. Downgrading BI-RADS 4a alone had higher AUC, specificity, and accuracy (P<0.01) and similar sensitivity (P>0.05) to upgrading BI-RADS 3 and downgrading BI-RADS 4a. Combining SE with BI-RADS could help reduce unnecessary biopsies by 17.64%-55.20%. Combining BI-RADS with SE improved the diagnostic performance in distinguishing benign from malignant lesions and could decrease false-positive breast biopsy rates. Downgrading BI-RADS 4a lesions alone might be sufficient for achieving good diagnostic performance. Downgrading BI-RADS category 4a lesions alone had higher AUC, specificity, and accuracy, and similar sensitivity to upgrading or downgrading BI-RADS category 3 and 4a lesions.

An NMR-based metabolic signature to identify clinically significant prostate cancer in patients undergoing biopsy.

Despite clinical suspicion of prostate cancer (PCa), 20-25% of patients exhibit a tumor-negative biopsy result. To assess the serum metabolic profile of clinically significant (cs) compared to clinically insignificant (ci) PCa or benign (Be) patients. 1078 serum samples were analyzed. Nuclear magnetic resonance (NMR) spectroscopy was used to quantify 73 metabolites, Random Forest for model algorithm. We identified a 22-metabolite panel, which discriminated csPCa (ISUP 2-5, n=328) from ciPCa (ISUP 1, n=101) and benign patients (negative biopsy, n=649) with a higher performance when combined with the standard clinical parameters age, prostate specific antigen (PSA), and percentage free PSA (%fPSA) (AUC 0.84) than the clinical parameters alone (AUC 0.73). Our study further revealed significant dysregulations of the urea cycle and the choline pathway along with changes in tricarboxylic acid (TCA) cycle, cholesterol metabolism, and a significant increase of the inflammation marker GlycB in csPCa patients. In particular, ornithine and dimethylglycine were the 2 most important features to discriminate csPCa from Be+ciPCa with significantly higher ornithine and lower dimethylglycine levels in patients with csPCa (ornithine: 63.7 ± 26.5 µmol/l, dimethylglycine: 12.6 ± 6.3 µmol/l, p<0.001) compared to Be+ciPCa patients (ornithine: 50.3 ± 31.6 µmol/l, dimethylglycine: 14.9 ± 7.7 µmol/l). This study discovered a 22-metabolite panel to discriminate patients with csPCa from Be+ciPCa patients when combined with age, PSA, and %fPSA. It may therefore be used as supportive biomarker to reduce the number of unnecessary biopsies and also to identify novel therapeutic targets in the future.

A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.

Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investigated whether a deep learning (DL) model can predict dysplasia probability among patients with leukoplakia using oral photographs. In addition, we assessed the performance of the DL model in comparison with clinicians' ratings and in providing decision support on dysplasia assessment. Retrospective images of leukoplakia taken before biopsy/histopathology were obtained to construct the DL model (n = 2,073). OED status following histopathology was used as the gold standard for all images. We first developed, fine-tuned, and internally validated a DL architecture with an EfficientNet-B2 backbone that outputs the predicted probability of OED, OED status, and regions-of-interest heat maps. Then, we tested the performance of the DL model on a temporal cohort before geographical validation. We also assessed the model's performance at external validation with opinions provided by human raters on OED status. Performance evaluation included discrimination, calibration, and potential net benefit. The DL model achieved good Brier scores, areas under the curve, and balanced accuracies of 0.124 (0.079-0.169), 0.882 (0.838-0.926), and 81.8% (76.5-87.1) at testing and 0.146 (0.112-0.18), 0.828 (0.792-0.864), and 76.4% (72.3-80.5) at external validation, respectively. In addition, the model had a higher potential net benefit in selecting patients with OL for biopsy/histopathology during OED assessment than when biopsies were performed for all patients. External validation also showed that the DL model had better accuracy than 92.3% (24/26) of human raters in classifying the OED status of leukoplakia from oral images (balanced accuracy: 54.8%-79.7%). Overall, the photograph-based intelligent model can predict OED probability and status in leukoplakia with good calibration and discrimination, which shows potential for decision support to select patients for biopsy/histopathology, obviate unnecessary biopsy, and assist in patient self-monitoring.

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

Pre-clinical study of IR808 dye for cervical cancer in vitro and in vivo imaging.

There is an urgent need for improved methods for early screening and rapid diagnosis of cervical cancer since current conventional screening methods are plagued by operator subjectivity and unnecessary biopsies. IR808 is a tumour-targeting near-infrared (NIR) fluorescent dye that permits NIR imaging without the requirement of chemical conjugation. Our study investigates an IR808-based strategy for real-time monitoring of the cervix in vivo and rapid assessment of cervical specimens in vitro. We investigated the uptake of IR808 in vitro using normal cervical epithelial cells and three cervical cancer cell lines. The biodistribution of IR808 was examined in vivo via intravenous injection into tumour-bearing mice. Additionally, in vitro tissues were stained with IR808 to simulate the identification of cervical tumors in the clinical setting. Biocompatibility of the dye in both cellular and animal models was also examined. IR808 exhibited significant tumour-to-background ratios in fluorescence molecular imaging of in vivo tumors in nude mice. The application of NIR fluorescent dye IR808 in specific imaging screening, safe and non-invasive real-time monitoring, and rapid identification of cervical tumors from tissue specimens is expected to improve current screening methods for cervical cancer.

Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.

Combination of Different Sectional Elastography Techniques with Age to Optimize the Downgrading of Breast BI-RAIDS Class 4a Nodules.

This study aims to optimize the downgrading of BI-RADS class 4a nodules by combining various sectional elastography techniques with age. We performed conventional ultrasonography, strain elastography (SE), and shear wave elastography (SWE) on patients. Quantitative parameters recorded included age, cross-sectional and longitudinal area ratios (C-EI/B, L-EI/B), strain rate ratios (C-SR, L-SR), overall average elastic modulus values (C-Emean1, L-Emean1), five-point average elastic modulus values (C-Emean2, L-Emean2), and maximum elastic modulus values (C-Emax, L-Emax). Histopathological evaluations showed that out of 230 lesions, 45 were malignant, and 185 were benign. The sensitivity and specificity of conventional ultrasonography were 100% and 0%, respectively. In contrast, SE and SWE exhibited higher specificity but lower sensitivity. Crosssectional parameters (C-EI/B, C-SR, C-Emean1, C-Emean2, and C-Emax) outperformed their longitudinal counterparts, with C-SR and C-Emax showing the highest specificity (72.43% and 73.51%) and satisfactory sensitivity (80.00% and 88.89%). Combining age with C-SR and C-Emax significantly improved diagnostic efficiency, achieving a sensitivity of 97.78% and a specificity of 77.30%. Integrating age with C-SR and C-Emax effectively reduces unnecessary biopsies for most BI-RADS 4a benign lesions while maintaining a very low misdiagnosis rate.

Inter-reader reliability and diagnostic accuracy of PI-RADS scoring between academic and community care networks: How wide is the gap?

ImportanceThe Prostate Imaging Reporting & Data System (PI-RADS) scoring guidelines were developed to address the substantial variation in interpretation and reporting of prostate cancer (PCa) multiparametric MRI (mpMRI) results, and subsequent updates have sought to further improve inter-reader reliability. Nonetheless, the variability of PI-RADS scoring in real-world settings may represent a continuing challenge to the widespread standardization of prostate mpMRI and limit its overall clinical benefit. ObjectiveTo assess variability in mpMRI interpretation and reporting of PCa, we evaluated the discrepancies in PI-RADS scoring between community practices and a tertiary academic care center. Design, setting, and participantsWe identified 262 mpMRI studies from nonacademic facilities, reinterpreted by radiologists at our institution between January 2016 and July 2022. Results of targeted MRI fusion biopsy were identified for 193 of these patients, totaling 302 lesions. PI-RADS scoring from both community and academic interpreters were recorded in addition to presence of clinically significant PCa (csPCa) on pathological analysis of targeted cores. Main Outcome and measuresThe primary outcome was inter-reader reliability via intraclass correlation (ICC) and the kappa statistic. We also assessed the diagnostic accuracy of PI-RADS scoring for detecting csPCa for both cohorts via receiver operator characteristics (ROC) analysis and compared these findings using paired-sample area difference under curve analysis. ResultsInter-reader agreement and reliability of PI-RADS scoring per lesion was generally poor (absolute agreement ICC = 0.393, 95% CI: 0.288–0.488; consistency ICC = 0.407, 95% CI: 0.308–0.497; kappa = 0.336, 95% CI: 0.267–0.406). Reliability results from studies obtained after the publication of PI-RADSv2.1 were similar to those of the overall analysis. No agreement was observed in the subgroup of lesions scored as PIRADS 3 by community interpreters. No statistically significant difference in diagnostic accuracy was observed between cohorts (ROC area under curve [AUC]: 0.759 vs. 0.785, respectively; P = 0.337). PI-RADS 3 was determined to be the optimal cutoff for detecting clinically significant disease in both cohorts. Conclusions and relevanceOur results suggest that mpMRI diagnostic accuracy for detecting csPCa is not significantly different between academic and community practices. However, significantly poor reliability of mpMRI was observed between cohorts, suggesting the risk of introducing practice variation for community prostate cancer management. Variability, particularly for PI-RADS 3 lesions, can lead to inconsistent biopsy recommendations, which may result in missed csPCa or unnecessary biopsies.

Unlocking Precision Enhancing Prostate Cancer Detection and Reducing Unnecessary Biopsies with Combined Prostate-Specific Antigen Density and PI-RADS Score.

To determine clinically significant prostate cancer (csPCa) detection rate by combining the prostate-specific antigen density (PSAD) and prostate imaging-reporting and data system (PI-RADS) scores. Descriptive study. Place and Duration of the Study: Department of Urology, University of Health Sciences, Ankara Oncology Training and Research Hospital, from January 2018 to April 2023. Patients who underwent prostate biopsies after multiparametric magnetic resonance imaging (mpMRI) were included in the study. PI-RADS 4 and 5 lesions were considered as MR positive. The cut-off values for PSAD were also determined to evaluate csPCa. csPCa detection rates were evaluated by grouping the patients based on the PSAD and mpMRI findings. PSAD cut-off value of 0.165 ng/mL/mL (sensitivity 80%, specificity 72%) was detected to predict csPCa (AUC = 0.81, 95% CI:0.756-0.866, p<0.001). csPCa detection rate was low (3%) in patients who have low PI-RADS scores (1-3) and a PSAD <0.165 ng/mL/mL. On the other hand, csPCa detection rate was high (50.5%) in patients who have a high PI-RADS score (4-5 lesions) and with a PSAD ≥0.165 ng/mL/mL. csPCa detection rates are low in patients with PI-RADS 1-3 lesions and low PSAD values. Unnecessary biopsy may be avoided in these patients. Gleason score, PI-RADS, Prostate cancer, Prostate-specific antigen, Prostate-specific antigen density.

A Study of Onychoscopic Findings in Various Dermatological Disorders

Context: About 10% of all the dermatological conditions are comprised nail disorders. It is challenging for the dermatologists to diagnose nail disorders quickly without the help of invasive procedures. Aims: The aim of the study was to evaluate onychoscopic patterns associated with various nail disorders. Settings and Designs: Hospital-based cross-sectional observational study was conducted at a tertiary care teaching institute in Maharashtra, India from November 2019 to May 2021. Materials and Methods: One hundred and fifty patients, with a history and clinical presentation suggestive of nail disorders, were recruited in the study after confirming the clinical diagnosis. Patients having secondarily modified lesions were excluded. Dry as well as wet onychoscopy using polarized and nonpolarized modes was performed with the help of DermLite 4 dermoscope with an attached OnePlus 8T mobile phone camera to capture and save the dermoscopic images. Data were compiled and statistically analyzed using SPSS version 21.0. Statistical Analysis Used: Categorical variables were expressed in frequency and percentages. Data were entered in Microsoft Excel and analyzed using SPSS version 20. P value (using Z-test for proportions) was calculated wherever needed. Results: The majority of the patients were in the age group of 21–50 years with slight male preponderance. Overall most common onychoscopic findings studied in all the disorders were the nail plate alterations followed by chromonychia. The most common nail changes associated with connective tissue disorders were changes in the proximal nail fold capillaries along with changes in the curvature. The most common change observed in onychomycosis was chromonychia. Aurora borealis, spikes, and jagged edges of onycholytic plate with ruin pattern of hyperkeratosis were specific to onychomycosis. Inflammatory disorders of the nail unit (e.g., lichen planus and nail psoriasis) showed nail plate alterations with increased nail plate fragility. Conclusions: Some onychoscopic patterns are observed consistently with certain diseases, and these can be used for their diagnosis, hence, avoiding unnecessary biopsies, thus improving the patient outcome.

Remote cutaneous confocal microscopy: A multicentric prospective study evaluating diagnostic accuracy for melanoma and keratinocyte carcinoma in tertiary settings

Cutaneous confocal microscopy (CCCM) facilitates in vivo visualisation of skin at a cellular level. Use of a "store and forward" approach for remote CCM interpretation (Remote-CCM) across multiple sites has not been tested and may increase access to non-invasive diagnosis. To test the diagnostic accuracy and safety of Remote-CCM. We prospectively recruited lesions selected for biopsy for skin malignancy across five Australian tertiary dermatology centres. CCM, clinical and dermoscopy images were acquired pre-biopsy and accessed by a cloud-based platform for interpretation by CCM readers. CCM diagnosis was compared to histopathology results. Amongst the 201 lesions included, melanoma was the most common malignancy (34/72, 47.2%). Of the 89 lesions (44.8%) potentially 'saved' from biopsy, 80 (90%) were truly benign lesions and 9 (10.1%) were missed malignant lesions of MIS (n=7) and SCC (n=2). No invasive melanomas were missed. Sensitivity of remote-CCM for detection of malignancy was 89% (95%CI 79-95%) and specificity was 64% (95%CI 55-73%). The study recruited from high-risk populations and excluded lesions that were not biopsied. Remote-CCM has comparable accuracy to bedside-CCM and safely reduces unnecessary biopsies. Potential SCCs are not appropriate for remote-CCM. Follow-up of borderline melanocytic lesions is recommended.

A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study.

The objective of this study was to develop a combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound (CEUS) during the Kupffer phase and to evaluate its value in differentiating well-differentiated hepatocellular carcinoma (w-HCC) from atypical benign focal liver lesions (FLLs). A total of 116 patients with preoperatively Sonazoid-CEUS confirmed w-HCC or benign FLL were selected from a prospective multiple study on the clinical application of Sonazoid in FLLs conducted from August 2020 to March 2021. According to the randomization principle, the patients were divided into a training cohort and a test cohort in a 7:3 ratio. Seventy-nine patients were used for establishing and training the radiomics model and combined model. In comparison, 37 patients were used for validating and comparing the performance of the models. The diagnostic efficacy of the models for w-HCC and atypical benign FLLs was evaluated using ROCs curves and decision curves. A combined model nomogram was created to assess its value in reducing unnecessary biopsies. Among the patients, there were 55 cases of w-HCC and 61 cases of atypical benign FLLs, including 28 cases of early liver abscess, 16 cases of atypical hepatic hemangioma, 8 cases of hepatocellular dysplastic nodules (DN), and 9 cases of focal nodular hyperplasia (FNH). The radiomics model and combined model we established had AUCs of 0.905 and 0.951, respectively, in the training cohort, and the AUCs of the two models in the test cohort were 0.826 and 0.912, respectively. The combined model outperformed the radiomics feature model significantly. Decision curve analysis demonstrated that the combined model achieved a higher net benefit within a specific threshold probability range (0.25 to 1.00). A nomogram of the combined model was developed. The combined model based on the radiomics features of Sonazoid-CEUS in the Kupffer phase showed satisfactory performance in diagnosing w-HCC and atypical benign FLLs. It can assist clinicians in timely detecting malignant FLLs and reducing unnecessary biopsies for benign diseases.

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions

IntroductionThe objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters. MethodsThe study included patients who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy between January 2020 and January 2024. Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose tissue thickness, lesion size, prostate volume) and clinical parameters (age, body mass index, total prostate specific antigen, free PSA, PSA density, systemic inflammatory index, neutrophil-lymphocyte ratio [NLR], platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented. The probability of prostate cancer prediction in PI-RADS 3 lesions was calculated using 6 different machine-learning models, with the input parameters being the aforementioned variables. ResultsOf the 235 participants in the trial, 61 had malignant fusion biopsy pathology and 174 had benign pathology. Among 6 different machine learning algorithms, the random forest model had the highest accuracy (0.86±0.04; 95% CI 0.85–0.87), F1 score (0.91±0.03; 95% CI 0.91–0.92) and AUC value (0.92±0.06; 95% CI 0.88–0.90). In SHAP analysis based on random forest model, tumour ADC, tumour ADC/contralateral ADC ratio and PSA density were the 3 most successful parameters in predicting malignancy. On the other hand, systemic inflammatory index and neutrophil lymphocyte ratio showed higher accuracy in predicting malignancy than total PSA, age, free PSA/total PSA and lesion size in SHAP analysis. ConclusionAmong the machine learning models we developed, especially the random forest model can predict malignancy in PI-RADS 3 lesions and prevent unnecessary biopsy. This model can be used in clinical practice with multicentre studies including more patients.