Abdominal Radiology最新文献

Purpose

Investigate spleen diameter (d) and volume (v) relationship in patients with hematologic malignancies (HM) by determining volumetric thresholds that best correlate to established diameter thresholds for assessing response to treatment. Exploratorily, interrogate the impact of volumetric measurements in response categories and as a predictor of response.

Methods

Secondary analysis of prospectively collected clinical trial data of 382 patients with HM. Spleen diameters were computed following Lugano criteria and volumes using deep learning segmentation. d and v relationship was estimated using power regression model, volumetric thresholds ((:{v}_{threshold})) for treatment response estimated; threshold search to determine percentual change ((:{v}_{%}),) and minimum volumetric increase ((:{v}_{increase})) that maximize agreement with Lugano criteria performed. Spleen diameter and volume predictive performance for clinical response investigated using random forest model.

Results

(:v=2.24times:{d}^{2.14}) describes the relationship between spleen diameter and volume. (:{v}_{threshold}) for splenomegaly was 546 cm³. (:{v}_{threshold}), (:{v}_{%}), and (:{v}_{increase}) for assessing response resulting in highest agreement with Lugano criteria were 570 cm³, 73%, and 170 cm³, respectively. Predictive performance for response between diameter and volume were not significantly different (P=0.78).

Conclusion

This study provides empirical spleen volume threshold and percentual changes that best correlate with diameter thresholds, i.e., Lugano criteria, for assessment of response to treatment in patients with HM. In our dataset use of spleen volumetric thresholds versus diameter thresholds resulted in similar response assessment categories and did not signal differences in predictive values for response.

Rectal cancer (RC) is one of the most common malignant tumors of the digestive system and has an alarmingly high incidence and mortality rate globally. Compared to conventional imaging examinations, radiomics can extract quantitative features that reflect tumor heterogeneity and mine data from medical images. In this review, we discuss the potential value of multimodal MRI-based radiomics in the diagnosis and treatment of RC, with a special emphasis on the role of peritumoral tissue characteristics in clinical decision-making. Existing studies have shown that a radiomics model integrating intratumoral and peritumoral characteristics has good application prospects in RC staging evaluation, efficacy prediction, metastasis monitoring, recurrence early warning, and prognosis judgment. At the same time, this paper also objectively analyzes the existing methodological limitations in this field, including insufficient data standardization, inadequate model validation, limited sample size and poor reproducibility of results. By combining existing evidence, this review aimed to enhance the attention of clinicians and radiologists on the characteristics of peritumoral tissues and promote the translational application of radiomics technology in the individualized treatment of RC.

Graphical abstract

Objectives

To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate cancer and to compare this against non-MRI models.

Methods

2895 MRIs were collected from two sources (1630 internal, 1265 public) in this retrospective study. Risk models compared were: Prostate Cancer Prevention Trial Risk Calculator 2.0, Prostate Biopsy Collaborative Group Calculator, PSA density, U-Net segmentation, and U-Net combined with clinical parameters. The reference standard was histopathology or negative follow-up. Performance metrics were calculated by simulating a triaging workflow compared to radiologist interpreting all exams on a test set of 465 patients. Sensitivity and specificity differences were assessed using the McNemar test. Differences in PPV and NPV were assessed using the Leisenring, Alonzo and Pepe generalized score statistic. Equivalence test p-values were adjusted within each measure using Benjamini–Hochberg correction.

Results

Triaging using U-Net with clinical parameters reduced radiologist workload by 12.5% with sensitivity decrease from 93 to 90% (p = 0.023) and specificity increase from 39 to 47% (p < 0.001). This simulated workload reduction was greater than triaging with risk calculators (3.2% and 1.3%, p < 0.001), and comparable to PSA density (8.4%, p = 0.071) and U-Net alone (11.6%, p = 0.762). Both U-Net triaging strategies increased PPV (+ 2.8% p = 0.005 clinical, + 2.2% p = 0.020 nonclinical), unlike non-U-Net strategies (p > 0.05). NPV remained equivalent for all scenarios (p > 0.05). Clinically-informed U-Net triaging correctly ruled out 20 (13.4%) radiologist false positives (12 PI-RADS = 3, 8 PI-RADS = 4). Of the eight (3.6%) false negatives, two were misclassified by the radiologist. No misclassified case was interpreted as PI-RADS 5.

Conclusions

Prostate MRI triaging using machine learning could reduce radiologist workload by 12.5% with a 3% sensitivity decrease and 8% specificity increase, outperforming triaging using non-imaging-based risk models. Further prospective validation is required.

Objective

The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs.

Methods

This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model.

Results

Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines.

Conclusion

The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model’s test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging.

Purpose

High-quality imaging is critical for accurate prostate cancer assessment using MRI. This study describes a quality improvement (QI) initiative for prostate MRI and evaluates the impact of image quality on diagnostic performance.

Methods

In this prospective study, 1328 patients underwent prostate MRI between March 2023 and March 2024. The QI initiative focused on patient preparation, protocol standardization, technologist education, and quality control. Image quality was prospectively scored using the Prostate Imaging Quality (PI-QUAL) system. Diagnostic performance was assessed by comparing the positive predictive value (PPV) of PI-RADS ≥ 3 lesions before and after the intervention and between exams with optimal (PI-QUAL ≥ 4) and suboptimal quality. Fisher’s exact test was used for comparisons. Clinically significant prostate cancer (csPCa) was defined as Gleason Grade Group ≥ 2.

Results

The proportion of PI-QUAL ≥ 4 exams increased from 67 to 84% after the intervention (p < 0.001). The PPV for any prostate cancer improved from 65 to 78% (p = 0.03), though the increase for csPCa (from 42 to 46%) was not statistically significant (p = 0.5). No significant difference in PPV was observed between optimal and suboptimal exams for any cancer (76% vs. 77%, p = 0.9) or csPCa (45% vs. 48%, p = 0.7).

Conclusion

The QI initiative significantly improved image quality and overall cancer detection rates. However, the association between image quality and csPCa detection was not statistically significant, possibly due to greater improvements in T2-weighted images vs. diffusion weighted images and high reader expertise.

Purpose

To evaluate the value of multiparametric MRI (mpMRI)-based habitat analysis for predicting prognoses in patients with high-grade serous ovarian cancer (HGSOC), and to develop combined models by integrating habitat analysis with clinical predictors.

Methods

This retrospective study included 503 HGSOC patients from four centers. A K-means algorithm was used to identify voxel clusters and generate habitats on mpMRI. Radiomics features were extracted from each habitat sub-region. After feature selection, habitat models were developed to predict overall survival (OS) and progression-free survival (PFS). Cox regression analyses were performed to identify clinical predictors and construct clinical models. Combined models were developed by integrating habitat signatures with clinical predictors. Model performance was evaluated using C-index and time-dependent receiver operating characteristic area under the curves (AUCs).

Results

Compared with the clinical models (OS: 0.713 and 0.695; PFS: 0.727 and 0.700) and habitat models (OS: 0.707 and 0.672; PFS: 0.627 and 0.641), the combined models integrating habitat features and clinical independent predictors such as neoadjuvant chemotherapy (OS: 0.752 and 0.745; PFS: 0.784 and 0.754) achieved the highest C-indices for predicting OS and PFS in the internal validation cohort and external test cohort. The combined models also achieved the highest AUCs in all cohorts.

Conclusion

The habitat models based on mpMRI demonstrated potential value in predicting the prognoses of HGSOC patients, but no significant advantages over the clinical models. The combined models were expected to improve the prognoses from the level of individual clinical characteristics and habitat features reflecting intratumoral heterogeneity.

Endometriosis is a chronic gynecological disorder characterized by the ectopic presence of endometrial tissue, often resulting in pelvic pain, infertility, and decreased quality of life. Magnetic Resonance Imaging (MRI) plays a crucial role in noninvasive diagnosis and preoperative assessment of endometriosis, particularly in evaluating complex or deep infiltrative diseases. A detailed and structured report on lesion depth, extension, and involvement of critical anatomical structures is vital for multidisciplinary teams’ decision-making. By comprehensively understanding and recognizing the complete range of endometriosis manifestations, radiologists can significantly enhance individualized treatment strategies and improve patient outcomes. This pictorial review highlights the key MRI features of endometriosis and provides essential guidance for radiologists during the imaging and reporting process.

Pancreas transplantation remains a key therapeutic option for patients with type 1 diabetes mellitus. Despite advances in medical management, post-transplant complications continue to pose significant risks to graft survival, making early detection essential for optimizing patient outcomes. Given the complexity of pancreas transplantation, multimodality imaging plays a central role in evaluating graft function and identifying complications. Each modality provides pertinent information based on the complication suspected. This review outlines imaging strategies essential for assessing graft viability, distinguishing expected postoperative findings from pathologic conditions, and recognizing early signs of graft dysfunction. It also highlights the expanding role of interventional radiology, emphasizing minimally invasive techniques for effectively managing post-transplant complications and promoting better graft survival. By integrating current imaging protocols with interventional methodologies, this review offers radiologists and transplant clinicians a practical framework to optimize clinical decision-making, improve complication management, and enhance long-term graft outcomes.