Diagnostic and interventional radiology最新文献

Purpose: To evaluate the diagnostic value of ultrasound in grading portal vein stenosis (PVS) in a rat model of 70% partial hepatectomy (PH).

Methods: A total of 96 Sprague-Dawley rats were randomly divided into a PH group and PVS groups with mild, moderate, and severe PVS following PH. Hemodynamic parameters were measured using high-frequency ultrasound (5-12 MHz high-frequency linear transducer), including pre-stenotic, stenotic, and post-stenotic portal vein diameters (PVD_pre, PVD_s, PVDpost); pre-stenotic and stenotic portal vein velocity (PVVpre, PVVs); hepatic artery peak systolic velocity (PSV); end-diastolic velocity; and resistive index. The portal vein diameter ratio (PVDR) and portal vein velocity ratio (PVVR) were calculated using the following formulas: PVDR=PVD_pre/PVD_s and PVVR=PVVs/PVVpre. The value of these parameters in grading PVS was assessed.

Results: Portal vein hemodynamics showed gradient changes as PVS aggravated. For identifying >50% PVS, PVD_s and PVDR were the best parameters, with areas under the curve (AUC) of 0.85 and 0.86, respectively. For identifying >65% PVS, PVD_s, PVDR, and PVVR were relatively better, with AUCs of 0.94, 0.85, and 0.88, respectively. The AUC of hepatic artery PSV for identifying >65% PVS was 0.733.

Conclusion: High-frequency ultrasonography can be used to grade PVS in rats, with PVD_s, PVDR, and PVVR being particularly useful. Hepatic artery PSV may help in predicting >65% PVS. These findings provide valuable information for PVS rat model research and offer an experimental basis for further studies on PVS evaluation in living-donor liver transplantation (LDLT).

Clinical significance: Ultrasonography serves as a first-line technology for diagnosing PVS following LDLT. However, the grading criteria for PVS severity remain unclear. Investigating the use of ultrasonic hemodynamics in the early diagnosis of PVS and grading stenosis severity is important for early postoperative intervention and improving recipient survival rates.

As an umbrella term, artificial intelligence (AI) covers machine learning and deep learning. This review aimed to elaborate on these terms to act as a primer for radiologists to learn more about the algorithms commonly used in musculoskeletal radiology. It also aimed to familiarize them with the common practices and issues in the use of AI in this domain.

Purpose: This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions.

Methods: This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.5, Google Gemini 1.5 Pro, Google Gemini 1.0, Microsoft Copilot, Perplexity, Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Opus 200K), general radiologists, and a breast radiologist using 100 text-based multiple-choice questions (MCQs) related to the BI-RADS Atlas 5^th edition. In the second step, we assessed the performance of five multimodal LLMs (ChatGPT 4o, ChatGPT 4V, Claude 3.5 Sonnet, Claude 3 Opus, and Google Gemini 1.5 Pro) in assigning BI-RADS categories and providing clinical management recommendations on 100 breast ultrasound images. The comparison of correct answers and accuracy by question types was analyzed using McNemar's and chi-squared tests. Management scores were analyzed using the Kruskal- Wallis and Wilcoxon tests.

Results: Claude 3.5 Sonnet achieved the highest accuracy in text-based MCQs (90%), followed by ChatGPT 4o (89%), outperforming all other LLMs and general radiologists (78% and 76%) (P < 0.05), except for the Claude 3 Opus models and the breast radiologist (82%) (P > 0.05). Lower-performing LLMs included Google Gemini 1.0 (61%) and ChatGPT 3.5 (60%). Performance across different categories of showed no significant variation among LLMs or radiologists (P > 0.05). For breast ultrasound images, Claude 3.5 Sonnet achieved 59% accuracy, significantly higher than other multimodal LLMs (P < 0.05). Management recommendations were evaluated using a 3-point Likert scale, with Claude 3.5 Sonnet scoring the highest (mean: 2.12 ± 0.97) (P < 0.05). Accuracy varied significantly across BI-RADS categories, except Claude 3 Opus (P < 0.05). Gemini 1.5 Pro failed to answer any BI-RADS 5 questions correctly. Similarly, ChatGPT 4V failed to answer any BI-RADS 1 questions correctly, making them the least accurate in these categories (P < 0.05).

Conclusion: Although LLMs such as Claude 3.5 Sonnet and ChatGPT 4o show promise in text-based BI-RADS assessments, their limitations in visual diagnostics suggest they should be used cautiously and under radiologists' supervision to avoid misdiagnoses.

Clinical significance: This study demonstrates that while LLMs exhibit strong capabilities in text-based BI-RADS assessments, their visual diagnostic abilities are currently limited, necessitating further development and cautious application in clinical practice.

Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.

Methods: A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve.

Results: In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, P = 0.009).

Conclusion: autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC.

Clinical significance: This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.

Purpose: The aim of the present study is to report the clinical results of patients with advanced intrahepatic cholangiocarcinoma (ICC) who received combination therapy of hepatic arterial infusion chemotherapy (HAIC), toripalimab and surufatinib.

Methods: The study cohort consisted of 28 patients with advanced ICC who were treated with HAIC (mFOLFOX6 regimen, Q3W) in combination with intravenous toripalimab (240 mg, Q3W) and oral surufatinib (150 mg, once daily). The cohort had 14 male and 14 female patients. The baseline characteristics of the study cohort were obtained. The tumor response and drug-associated toxicity were assessed and reported.

Results: During the follow-up period (median follow-up time: 11.3 months; range: 4-19 months), four patients died of tumor progression. The objective response rate and disease control rate were 58% and 79%, respectively. The mPFS was 9.5 months, and the overall survival rate was 83.3%. The most frequent adverse events were nausea and vomiting (100%) and abdominal pain (85.7%). Serious complications related to death were not observed.

Conclusion: The combination treatment schedule for advanced ICC demonstrated positive efficacy and safety profiles.

Clinical significance: This study provides promising clinical guidance for the treatment of advanced cholangiocarcinoma and is expected to modify the treatment strategy for this disease.

Purpose: To evaluate the efficacy and safety of Amplatzer Vascular Plug 4 (AVP4) embolization in pulmonary arteriovenous malformations (PAVMs) with small- to medium-sized feeding arteries (<6 mm) and to identify factors affecting persistence and the main persistence patterns after embolization.

Methods: Between June 2013 and February 2023, we retrospectively reviewed 100 patients with 217 treated PAVMs. We included PAVMs with feeding arteries <6 mm, treated with AVP4 embolization, and followed adequately with computed tomography (CT). Technical success was defined as flow cessation observed on angiography. Persistence was defined as less than a 70% reduction of the venous sac on CT. We evaluated adverse events for each embolization session. Patterns of persistence were assessed using follow-up angiography. Univariate and multivariate analyses were performed to evaluate factors affecting persistence based on the 70% CT criteria.

Results: Fifty-one patients (48 women, 3 men; mean age: 50.8 years; age range: 16-71 years) with 103 PAVMs met the inclusion criteria. The technical success rate was 100%. The persistence rate was 9.7% (10/103), and the overall adverse event rate was 2.9% (3/103) during a mean follow-up of 556 days (range: 181-3,542 days). In two cases, the persistence pattern confirmed by follow-up angiography involved reperfusion via adjacent pulmonary artery collaterals. The location of embolization relative to the last normal branch of the pulmonary artery was the only factor substantially affecting persistence.

Conclusion: Embolization with AVP4 appears to be safe and effective for small- to medium-sized PAVMs. The location of the embolization relative to the last normal branch of the pulmonary artery was found to be the main determinant of persistence.

Clinical significance: Given the increasing demand for the treatment of small PAVMs, AVP4 embolization could be considered a viable and effective option for managing PAVMs with feeding arteries <6 mm.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

The liver is a common location for both primary and secondary cancers of the abdomen. Radiologists become familiar with the typical imaging features of common benign and malignant liver tumors; however, many types of liver tumors are encountered infrequently. Due to the rarity of these lesions, their typical imaging patterns may not be easily recognized, meaning their underlying pathologic features may not be discovered or suggested until an invasive biopsy is performed. In this review article, we discuss multiple hepatic neoplasms that are both unusual and rare. Some have typical imaging patterns, whereas others are non-specific and can only be included in the differential diagnosis. The clinical history and serologic findings are often critical in suggesting these entities; therefore, these are also discussed to familiarize the radiologist with the appropriate clinical setting of each. The article includes an image-rich description of each entity with accompanying figures describing the ultrasonography, computed tomography, and magnetic resonance imaging features of each disease process. Novel therapies and prognosis of several of the diseases are also included in the discussion.

Purpose: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.

Methods: We retrospectively included abdominopelvic CT images with the following inclusion criteria: a) CT images from patients with solid organ malignancies between March 1 and March 31, 2019, in a single institution; and b) abdominal CT images interpreted as negative for basal lung metastases. Reference standards for diagnosis of lung metastases were confirmed by reviewing medical records and subsequent CT images. An AI system that could automatically detect lung nodules on CT images was applied retrospectively. A radiologist reviewed the AI detection results to classify them as lesions with the possibility of metastasis or clearly benign. The performance of the initial AI results and the radiologist's review of the AI results were evaluated using patient-level and lesion-level sensitivities, false-positive rates, and the number of false-positive lesions per patient.

Results: A total of 878 patients (580 men; mean age, 63 years) were included, with overlooked basal lung metastases confirmed in 13 patients (1.5%). The AI exhibited an area under the receiver operating characteristic curve value of 0.911 for the identification of overlooked basal lung metastases. Patient- and lesion-level sensitivities of the AI system ranged from 69.2% to 92.3% and 46.2% to 92.3%, respectively. After a radiologist reviewed the AI results, the sensitivity remained unchanged. The false-positive rate and number of false-positive lesions per patient ranged from 5.8% to 27.6% and 0.1% to 0.5%, respectively. Radiologist reviews significantly reduced the false-positive rate (2.4%-12.6%; all P values < 0.001) and the number of false-positive lesions detected per patient (0.03-0.20, respectively).

Conclusion: The AI system could accurately identify basal lung metastases detected in abdominopelvic CT images that were overlooked by radiologists, suggesting its potential as a tool for radiologist interpretation.

Clinical significance: The AI system can identify missed basal lung lesions in abdominopelvic CT scans in patients with malignancy, providing feedback to radiologists, which can reduce the risk of missing basal lung metastasis.

Transperineal prostate microwave thermoablation (TPMT) has been established as a safe means of treating benign prostatic hyperplasia (BPH); however, its effectiveness in addressing BPH-related lower urinary tract symptoms (LUTS) remains unexplored. This case study aims to evaluate the efficacy of TPMT in LUTS attributed to BPH. An 84-year-old man with LUTS due to BPH-induced bladder outlet obstruction, unresponsive to previous medical treatments, and failed prostate artery embolization, underwent TPMT. Three coaxial needles were positioned at the midline, right, and left sides of the hypertrophic transitional zone of the prostate. Microwave energy, with parameters determined using liver data and targeted ablation area, was applied at 2,450 MHz in continuous mode. The tissue temperature was monitored using bilateral thermocouple sensors. The patient exhibited no changes in defecation rhythm, abdominal discomfort, or anorectal pain. Temporary postoperative hematuria was promptly resolved through saline irrigation within 6 hours, and hematological evaluations showed normal results. Significant clinical improvements were observed (e.g., prostate volume, prostate-specific antigen levels) accompanied by an increase in peak flow rate. Thus, TPMT appears to be a promising intervention for bladder outlet stenosis and LUTS induced by BPH.