A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients.

Objective: BRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methods: In this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. Results: In the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647-0.776) vs 0.768 (95% CI, 0.704-0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597-0.808) vs 0.723 (95% CI, 0.625-0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748-0.861) in the training dataset and 0.811 (95% CI, 0.724-0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong's test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. Conclusion: This machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.

Diagnostic Performance of Modified Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Patients Without Risk Factors for Hepatocellular Carcinoma: Comparison With World Federation for Ultrasound in Medicine and Biology Guideline.

OBJECTIVE: The aim of this study was to assess the ability of the modified contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) to distinguish malignancy in patients without known hepatocellular carcinoma (HCC) risk factors and compare diagnostic accuracy with that of the World Federation for Ultrasound in Medicine and Biology (WFUMB) guideline across radiologists with different levels of CEUS experience. METHODS: A total of 848 individuals with no hepatitis infection presenting with 870 lesions in non-cirrhotic livers were included and divided into the Testing and Validation groups. The modified CEUS LI-RADS was proposed, including downgrading of focal nodular hyperplasia with typical features. Diagnostic performance of the modified CEUS LI-RADS was assessed in the Testing group. In the Validation group, two radiologists with more than 9 y of CEUS experience (Experts) and two radiologists with less than 6 mo of CEUS experience (Novices) used both the modified CEUS LI-RADS and the WFUMB guideline to evaluate performance in diagnosis of the lesions. RESULTS: LR-5 + M (combination of modified LR-5 and modified LR-M) revealed optimal performance with a sensitivity, specificity and area under the curve (AUC) of 99.3%, 81.6% and 0.904, respectively. Novices using the modified CEUS LI-RADS outperformed those using the WFUMB guideline (AUC: 0.858 vs. 0.767, p = 0.005). Additionally, the sensitivity, specificity and AUC of Novices were comparable to those of Experts using the modified CEUS LI-RADS (94.1%, 77.6% and 0.858 vs. 96.1%, 77.6% and 0.868 for experts, respectively). CONCLUSION: The modified CEUS LI-RADS is a valuable method for distinguishing hepatic malignancy in patients without HCC risk factors. This is particularly beneficial for radiologists with limited CEUS expertise.

Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.

BACKGROUND: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. METHODS: This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. FINDINGS: The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort. INTERPRETATION: The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. FUNDING: Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).

Vascular Diameters as Predictive Factors of Recanalization Surgery Outcomes in Internal Carotid Artery Occlusion.

Background: Revascularization surgery sometimes can achieve recanalization in patients with internal carotid artery occlusion (ICAO). High-resolution vessel wall magnetic resonance imaging (HRVWI) is a feasible technique to give detailed characteristics of the vessel wall, which may help to identify patients that carry higher success rates and more suitable for revascularization surgery. Objective: To examine the association between HRVWI characteristics of ICAO and the success rate of revascularization surgery in ICAO patients. Methods: We conducted a retrospective analysis of 31 ICAO recanalization patients enrolled from October 2017 to May 2019. The clinical data of patients and lesions were collected and analyzed. Results: A total of 31 ICAO patients were enrolled in this study. No significant differences were found between recanalization success and recanalization failure groups with regard to occlusion length, distal end of the occluded segment, and the treatment applied. The ipsilateral-to-contralateral diameter ratios (I/C ratios) of C1 or C2 and the diameter of C7 were positively related to recanalization success. A two-factor predictive model was constructed, and the I/C ratio of C2 < 0.86 and the diameter of C7 < 1.75mm were separately assigned 1 point. The ICAO patients who scored 0, 1, or 2 points had a risk of 5.6% (1/18), 55.6% (5/9), or 100% (4/4) to fail in the recanalization. Conclusions: The I/C ratios of C1 or C2 and the diameter of C7 are predictive factors of a revascularization surgery success in ICAO patients. A risk stratification model involving C2 and C7 was constructed for future clinical applications.

Association of a novel PKHD1 mutation in a family with autosomal dominant polycystic liver disease.

BACKGROUND: Autosomal dominant polycystic liver disease (ADPLD) is characterized by multiple cysts in the liver without (or only occasional) renal cysts. At least seven genes are associated with high risk for developing ADPLD; however, clear genetic involvement is undetermined in more than 50% of ADPLD patients. METHODS: To identify additional ADPLD-associated genes, we collected 18 unrelated Chinese ADPLD cases, and performed whole exome sequencing on all the participants. After filtering the sequencing data against the human gene mutation database (HGMD) professional edition, we identified new mutations. We then sequenced this gene in family members of the patient. RESULTS: Among the 18 ADPLD cases analyzed by whole exome sequencing, we found 2 cases with a PRKCSH mutation (~11.1%), 2 cases with a PKD2 mutation (~11.1%), 1 case with both PKHD1 and PKD1 mutations (~5.6%), 1 case with GANAB mutation (~5.6%), 1 case with PKHD1 mutation (~5.6%), and 1 case with PKD1 mutations (~5.6%). We identified a new PKHD1 missense mutation in an ADPLD family, in which both patients showed innumerable small hepatic cysts, as reported previously. Additionally, we found that PRKCSH and SEC63 mutation frequencies were lower in the Chinese population compared with those in European and American populations. CONCLUSIONS: We report a family with ADPLD associated with a novel PKHD1 mutation (G1210R). The genetic profile of ADPLD in the Chinese population is different from that in European and American populations, suggesting that further genetic research on genetic mutation of ADPLD in the Chinese population is warranted.