Background: The Xpert MTB/RIF Ultra (Xpert Ultra) was introduced to enhance the sensitivity of tuberculosis detection, particularly in smear-negative cases, compared with its predecessor, Xpert MTB/RIF (Xpert). However, its performance in high-resource, intermediateburden settings remains unassessed. We prospectively compared the diagnostic accuracy of Xpert Ultra and Xpert for detecting Mycobacterium tuberculosis (MTB) and rifampin resistance in Korea. Methods: In total, 309 respiratory specimens were analyzed using both assays. We used two reference standards: mycobacterial culture and a composite reference standard based on clinical diagnosis and treatment decisions. Diagnostic performance, including sensitivity, specificity, and agreement between the two assays, was assessed. Spiking experiments using 13 MTB isolates with known rpoB mutations were performed to evaluate rifampin resistance detection. Results: Xpert Ultra showed increased, albeit not significantly, sensitivity (73.7% vs. 65.8% with culture; 63.8% vs. 53.2% with the composite reference standard) over Xpert. Its specificity was comparable to that of Xpert; however, a few false-positive results were observed among trace- and very low-positives. Among six culture-negative but Xpert Ultra-positive cases, two were clinically diagnosed as tuberculosis. Of the 13 rpoB mutant strains, Xpert correctly detected all mutations in the rifampin resistance-determining region, whereas Xpert Ultra yielded indeterminate results for Q432P and Q429H/L430P/H445Q. Conclusions Xpert Ultra tends to have increased sensitivity; however, it shows potential diagnostic ambiguity associated with trace- or very low-positive results. These findings highlight the importance of clinical correlation, particularly in culture-negative cases. Indeterminate results in certain rpoB mutations require cautious interpretation.

Ligamentum flavum cysts (LFCs) are rare but clinically significant lesions in the lumbar spine. These cysts, often found incidentally, can mimic the symptoms of intervertebral disc herniation and cause radiculopathy. This article details a case involving an 80-year-old woman with severe lower back and radicular pain due to an LFC at the right L5/S1 facet joint that was unresponsive to conservative treatment. Magnetic resonance imaging and neurography confirmed that the cyst compressed the right S1 nerve root. Surgical excision via partial hemilaminectomy resulted in complete symptom relief and nerve decompression. Histopathological analysis identified the cyst as degenerative without a synovial lining. This case underscores the importance of considering LFCs in the differential diagnosis of neurogenic leg pain and highlights the efficacy of surgical excision for symptom resolution and preventing recurrence.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Purpose: The Korean Cancer Study Group (KCSG) is a nationwide cancer clinical trial group dedicated to advancing investigator-initiated trials (IITs) by conducting and supporting clinical trials. This study aims to review IITs conducted by KCSG and quantitatively evaluate the survival and financial benefits of IITs for patients. Materials and Methods: We reviewed IITs conducted by KCSG from 1998 to 2023, analyzing progression-free survival (PFS) and overall survival (OS) gains for participants. PFS and OS benefits were calculated as the difference in median survival times between the intervention and control groups, multiplied by the number of patients in the intervention group. Financial benefits were assessed based on the cost of investigational products provided. Results: From 1998 to 2023, KCSG conducted 310 IITs, with 133 completed and published. Of these, 21 were included in the survival analysis. The analysis revealed that 1,951 patients in the intervention groups gained a total of 2,558.4 months (213.2 years) of PFS and 2,501.6 months (208.5 years) of OS, with median gains of 1.31 months in PFS and 1.58 months in OS per patient. When analyzing only statistically significant results, PFS and OS gain per patients was 1.69 months and 3.02 months, respectively. Investigational drug cost analysis from six available IITs indicated that investigational products provided to 252 patients were valued at 10,400,077,294 won (approximately 8,046,481 US dollars), averaging about 41,270,148 won (approximately 31,930 US dollars) per patient. Conclusion Our findings, based on analysis of published research, suggest that IITs conducted by KCSG led to survival benefits for participants and, in some studies, may have provided financial benefits by providing investment drugs.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Purpose: The Korean Cancer Study Group (KCSG) is a nationwide cancer clinical trial group dedicated to advancing investigator-initiated trials (IITs) by conducting and supporting clinical trials. This study aims to review IITs conducted by KCSG and quantitatively evaluate the survival and financial benefits of IITs for patients. Materials and Methods: We reviewed IITs conducted by KCSG from 1998 to 2023, analyzing progression-free survival (PFS) and overall survival (OS) gains for participants. PFS and OS benefits were calculated as the difference in median survival times between the intervention and control groups, multiplied by the number of patients in the intervention group. Financial benefits were assessed based on the cost of investigational products provided. Results: From 1998 to 2023, KCSG conducted 310 IITs, with 133 completed and published. Of these, 21 were included in the survival analysis. The analysis revealed that 1,951 patients in the intervention groups gained a total of 2,558.4 months (213.2 years) of PFS and 2,501.6 months (208.5 years) of OS, with median gains of 1.31 months in PFS and 1.58 months in OS per patient. When analyzing only statistically significant results, PFS and OS gain per patients was 1.69 months and 3.02 months, respectively. Investigational drug cost analysis from six available IITs indicated that investigational products provided to 252 patients were valued at 10,400,077,294 won (approximately 8,046,481 US dollars), averaging about 41,270,148 won (approximately 31,930 US dollars) per patient. Conclusion Our findings, based on analysis of published research, suggest that IITs conducted by KCSG led to survival benefits for participants and, in some studies, may have provided financial benefits by providing investment drugs.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.