RATIONALE AND OBJECTIVE:The study aimed to develop and validate a multimodal radiomics model that integrates radiologist-informed feature augmentation leveraging expert-selected suspicious lymph nodes (LNs) based on ESGAR criteria to improve the accuracy of preoperative lymph node metastasis (LNM) prediction in patients with rectal cancer (RC). MATERIALS AND METHODS:This retrospective study included 563 eligible patients with RC. From each patient's high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI) sequences, we extracted radiomic features from three distinct regions: the primary tumor, the entire mesorectal nodal region, and suspicious mesorectal nodes identified by radiologists. Clinical factors associated with LNM were identified through univariate and multivariate logistic regression analyses to establish a clinical prediction model. Finally, we constructed an integrated predictive model by combining these clinical factors with multimodal radiomic features, followed by a comprehensive comparison and evaluation of the predictive performance across all developed models. RESULTS:The integrated model, incorporating radiomic features derived from DWI sequences of the entire mesorectal nodal region and radiologist-annotated suspicious LNs, along with clinical factors, achieved optimal performance in predicting LNM. It yielded an area under the curve of 0.87 (95% confidence interval [CI]: 0.83-0.90) in the internal validation cohort and 0.83 (95% CI: 0.78-0.89) in the external validation cohort. CONCLUSION:Our findings show that the multimodal radiomics model integrating radiologists' prior knowledge offers potential for improving preoperative LNM assessment in RC, particularly in internal validation, and may provide supportive information for personalized treatment strategies in clinical practice. However, the incremental benefit of the radiologist-informed component was not consistently demonstrated in external validation, and further multi-center prospective studies are warranted.

OBJECTIVE:To assess the survival benefit of synchronous systemic therapy plus thermal ablation (TA) in oligometastatic colorectal lung metastases (CRLM) and identify independent prognostic factors. BACKGROUND:Optimizing the integration of systemic therapy and TA for potentially curable CRLM remains a significant clinical challenge. METHODS:This study employed a retrospective cohort design, including 326 patients who underwent TA treatment at six tertiary medical centers from March 2014 to October 2022. Patients were categorized into synchronous therapy, upfront ablation, delayed ablation, and no systemic therapy groups based on the timing of systemic therapy relative to TA. Kaplan-Meier analysis and log-rank tests were used to assess survival outcomes. RESULTS:Synchronous systemic therapy yielded the longest median progression-free survival (PFS) (22.0 months) and overall survival (OS) (61.3 months) compared to delayed ablation (13.0 and 49.2 months, respectively) and no systemic therapy (11.9 and 29.3 months, respectively) (all p < 0.05). Synchronous systemic therapy was an independent protective factor for PFS [hazard ratio (HR) = 0.493] and OS (HR = 0.211). Independent risk factors for local tumor progression included tumor size ≥3 cm (HR = 1.75) and peridiaphragmatic location (HR = 1.48). For PFS, independent predictors included tumor numbers (p < 0.001), synchronous metastases (HR = 1.431), and extrapulmonary metastases (p = 0.001). OS was adversely influenced by tumor burden (p < 0.05), extrapulmonary metastases (p < 0.001), and mediastinal lymph node involvement (HR = 1.518). CONCLUSIONS:Synchronous systemic therapy combined with TA significantly enhances PFS and OS in potentially curable oligometastatic CRLM patients.

ObjectiveTo evaluate if transarterial chemoembolization (TACE) combined with Icaritin provides additional survival benefits compared to TACE alone in intermediate-to-advanced hepatocellular carcinoma (HCC) across different Child-Pugh classes.BackgroundTACE is a standard locoregional therapy for intermediate-to-advanced HCC, yet monotherapy yields limited long-term survival. Icaritin, an immunomodulator, demonstrates survival benefits in advanced HCC with a favorable safety profile lacking severe hepatotoxicity or bone marrow suppression.MethodsThis multicenter retrospective cohort study included patients with Barcelona Clinic Liver Cancer (BCLC) stage B and C HCC. Propensity score matching (PSM) was utilized to balance baseline covariates between the TACE with Icaritin and TACE alone groups. The primary endpoint was overall survival (OS).ResultsFollowing PSM, 250 patients were evaluated. In the Child-Pugh grade A group, TACE combined with Icaritin was associated with longer median OS than TACE alone (28.8 vs. 15.7 months; HR, 0.42; 95% CI, 0.29-0.62; P<0.001). Median PFS was also longer in the combination group (9.7 vs. 8.1 months; HR, 0.66; 95% CI, 0.49-0.90; P = 0.007). In Child-Pugh subgroup analyses, the treatment effect was most consistent in patients with Child-Pugh grade A liver function. In Child-Pugh grade B patients, OS favored the combination therapy, whereas the PFS result was not statistically definitive. ORR (49.6% vs. 47.2%) and DCR (83.2% vs. 74.4%) were comparable. The combination regimen did not significantly increase grade 3–4 liver function-related adverse events.ConclusionsTACE combined with Icaritin was associated with improved survival outcomes, with the most consistent benefit observed in patients with Child-Pugh grade A liver function. In Child-Pugh grade B patients, the findings should be interpreted cautiously because of the limited subgroup size and insufficient statistical power.

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semiautomatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This article systematically reviews methods for the automatic and semiautomatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.

To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1-Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment.

Graft impingement is a critical cause of anterior cruciate ligament reconstruction (ACLR) failure. Identifying its contributing factors is essential for improving surgical outcomes. This retrospective study aimed to evaluate the incidence of graft impingement following ACLR using magnetic resonance imaging (MRI) and to investigate potential anatomical and surgical risk factors. The findings are intended to provide theoretical support for reducing impingement rates and enhancing functional recovery. We retrospectively reviewed clinical and MRI data of 122 patients (68 males and 54 females) who underwent ACLR at our institution from January 2015 to December 2023. MRI was used to identify graft impingement and to measure potential anatomical and surgical factors, including graft angle, posterior tibial slope, tibial intercondylar eminence angle, intercondylar notch width, notch height, and roof inclination, tibial tunnel position, preoperative and postoperative tibial displacement (measured as anterior tibial translation), and concomitant injuries. Patients were categorized based on the presence or absence of impingement. Univariate analysis was followed by multivariable logistic regression to identify independent risk factors. Graft impingement occurred in 65 patients (53.3% of cases). Multivariable logistic regression revealed that smaller graft angles (odds ratio [OR] = 0.930, 95% confidence interval [CI]: 0.873-0.991, p = 0.026), anterior-inferior osteophytes of the intercondylar notch roof (OR = 3.620, 95% CI: 1.408-9.311, p = 0.008), bony abnormalities at the tibial tunnel inlet (OR = 3.814, 95% CI: 1.509-9.632, p = 0.005) and postoperative tibial displacement >5 mm (OR = 6.573, 95% CI: 1.120-38.582, p = 0.037) were independent risk factors for graft impingement. Graft impingement after ACLR is independently associated with reduced graft angle, anterior-inferior osteophytes of the intercondylar notch, excessive postoperative tibial displacement, and bony protrusions at the tibial tunnel inlet. These findings emphasize the importance of accurate tunnel positioning and anatomical assessment during surgery to improve patient outcomes.

BACKGROUND:Colorectal cancer (CRC) frequently metastasizes to the lungs, and image-guided thermal ablation (IGTA) has emerged as a promising treatment for oligometastatic colorectal lung metastases (CRLM). However, high-quality multicenter data remain limited, and the prognostic impact of site-specific extrapulmonary metastases is not well defined. AIM:To assess IGTA efficacy in potentially curable oligometastatic CRLM and determine prognostic impacts of extrapulmonary metastatic patterns. METHODS:This multicenter real-world study analyzed 336 CRLM patients treated with IGTA from 2014 to 2022. Inclusion criteria included pathologically or clinically confirmed oligometastatic CRC, tumor diameter < 50 mm, fewer than 5 metastatic lesions, and ≤ 2 organs involved. Kaplan-Meier and Cox regression methods assessed survival outcomes, including local tumor progression-free survival, progression-free survival (PFS), and overall survival (OS). RESULTS:The 3-year cumulative local tumor progression rate was 14.0%. Median PFS and OS were 15.6 and 51 months, respectively, with 3- and 5-year OS rates of 59.5% and 41.0%. Poor survival outcomes were associated with a higher tumor burden (larger size and greater number), carcinoembryonic antigen > 20 ng/mL, carbohydrate antigen 19-9 > 37 U/mL, and extrapulmonary metastases. Patients without extrapulmonary metastasis had 1-, 3-, and 5-year PFS rates of 65.4%, 31.0%, and 27.3%, respectively, which were longer than those of CRLM patients with liver metastasis [hazard ratio (HR) = 1.449, P = 0.019] and abdominal cavity metastasis (HR = 1.864, P = 0.010). The 1-, 3-, and 5-year OS rates for patients without extrapulmonary metastasis were 96.4%, 71.0%, and 53.0%, respectively, which were significantly longer than those for patients with bone metastasis (HR = 4.538, P < 0.001), abdominal cavity metastasis (HR = 4.813, P < 0.001), and pelvic cavity metastasis (HR = 3.105, P < 0.001). CONCLUSION:Metastatic patterns significantly influence PFS and OS, emphasizing the need for careful patient selection. Notably, patients with liver-only extrapulmonary metastasis demonstrate comparatively favorable outcomes, suggesting a distinct biological behavior and better prognosis within this subgroup.

BACKGROUND:To investigate the prognostic value of RAS mutation status and subtypes in colorectal lung metastases (CRLM) patients undergoing image-guided thermal ablation (IGTA), and to evaluate survival outcomes under different systemic therapy regimens. MATERIALS AND METHODS:In this multicenter retrospective-prospective cohort study, 387 patients with CRLM who received percutaneous IGTA between March 2014 and December 2022 were included. Patients were stratified by RAS genotype (KRAS/NRAS wild-type vs mutant). Survival outcomes including local tumor progression-free survival (LTPFS), progression-free survival (PFS), and overall survival (OS) were analyzed using Cox regression. Subgroup analyses were conducted based on chemotherapy regimens and targeted agents. RESULTS:The 3-year LTP rate was significantly higher in KRAS-mutant patients (25.0%) than wild-type (15.0%). KRAS mutation, lesion diameter ≥20 mm, and elevated CEA were independent risk factors for LTP. Median PFS was 16.0 months; KRAS mutations predicted inferior PFS (13.3% vs 32.8% at 3 years). Median OS was significantly reduced in both KRAS (17.9 vs 56.8 months) and NRAS-mutant patients (22.1 vs 53.8 months). Among KRAS-mutant patients, FOLFOX plus bevacizumab yielded better OS than cetuximab. CONCLUSION:RAS mutations are independent predictors of poor local control and survival after IGTA in CRLM. Evaluate interactions between RAS genotype and commonly used targeted agents in the peri-ablative setting. These integrated, real-world insights support the development of genotype-guided ablation planning and peri-ablative systemic therapy strategies.

Purpose: To develop and validate an accurate computed tomography-based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM). Materials and Methods: We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non-high-grade groups and matched using PSM. Matched patient HRCT images were used to delineate regions of interest from tumors and extract radiomics features, and the random forest method was used to construct a radiomics model. The area under the receiver operating characteristic curve (area under the curve) was used to evaluate the model's performance, and external validation was performed to assess the model's generalizability. Results: Before PSM, there was no statistically significant difference in age between the two groups, though nodule type and sex exhibited significant differences (P < 0.05) in both cohorts. After PSM, we matched 176 and 97 pairs of patients in the 2 cohorts. In both cohorts, sex and nodule type were equal between the two groups, with a higher percentage of males and solid nodules in both groups. Our model exhibited moderate predictive performance after PSM, with area under the curve values of 0.75 (95% CI: 0.70-0.80) and 0.71 (95% CI: 0.63-0.80) for the development and external validation cohorts, respectively. Conclusion: Although the nodule type compromised the validity of the model's performance, our results suggest that our acute computed tomography-based radiomics model could preoperatively predict micropapillary/solid patterns in patients with stage I lung IAC after PSM.

To develop and validate an accurate computed tomography–based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM). We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non–high-grade groups and matched using PSM. Matched patient HRCT images were used to delineate regions of interest from tumors and extract radiomics features, and the random forest method was used to construct a radiomics model. The area under the receiver operating characteristic curve (area under the curve) was used to evaluate the model’s performance, and external validation was performed to assess the model’s generalizability. Before PSM, there was no statistically significant difference in age between the two groups, though nodule type and sex exhibited significant differences (P < 0.05) in both cohorts. After PSM, we matched 176 and 97 pairs of patients in the 2 cohorts. In both cohorts, sex and nodule type were equal between the two groups, with a higher percentage of males and solid nodules in both groups. Our model exhibited moderate predictive performance after PSM, with area under the curve values of 0.75 (95% CI: 0.70-0.80) and 0.71 (95% CI: 0.63-0.80) for the development and external validation cohorts, respectively. Although the nodule type compromised the validity of the model’s performance, our results suggest that our acute computed tomography–based radiomics model could preoperatively predict micropapillary/solid patterns in patients with stage I lung IAC after PSM.

Aims/Background In the treatment of patients with cervical cancer, lymph node metastasis (LNM) is an important indicator for stratified treatment and prognosis of cervical cancer. This study aimed to develop and validate a multimodal model based on contrast-enhanced multiphase computed tomography (CT) images and clinical variables to accurately predict LNM in patients with cervical cancer. Methods This study included 233 multiphase contrast-enhanced CT images of patients with pathologically confirmed cervical malignancies treated at the Affiliated Dongyang Hospital of Wenzhou Medical University. A three-dimensional MedicalNet pre-trained model was used to extract features. Minimum redundancy-maximum correlation, and least absolute shrinkage and selection operator regression were used to screen the features that were ultimately combined with clinical candidate predictors to build the prediction model. The area under the curve (AUC) was used to assess the predictive efficacy of the model. Results The results indicate that the deep transfer learning model exhibited high diagnostic performance within the internal validation set, with an AUC of 0.82, accuracy of 0.88, sensitivity of 0.83, and specificity of 0.89. Conclusion We constructed a comprehensive, multiparameter model based on the concept of deep transfer learning, by pre-training the model with contrast-enhanced multiphase CT images and an array of clinical variables, for predicting LNM in patients with cervical cancer, which could aid the clinical stratification of these patients via a noninvasive manner.

BackgroundThe novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.MethodsWe retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.ResultsThe AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).ConclusionThe nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Background This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Abstract Background The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground‐glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. Methods We developed and validated a three‐dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre‐training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end‐to‐end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. Results The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). Conclusions Our 3D deep transfer learning model provides a noninvasive, low‐cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.

Purpose To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs).MethodsWe retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect.ResultsThere was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities.ConclusionsThe radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.

Aggregatibacter aphrophilus is part of the normal flora in the oropharynx and upper respiratory tract, which causes invasive bacteremia in rare cases. However, the culture and identification of Aggregatibacter aphrophilus are challenging, hence easily misdiagnosed or undetected in clinical practice. In this case, a 73-year-old male patient was admitted to the hospital with a fever and right hip pain. Routine blood and C-reactive protein tests showed abnormal inflammatory markers. Positive blood culture revealed the presence of Aggregatibacter aphrophilus through mass spectrometry. The computed tomography examination further revealed the presence of psoas abscess, pulmonary infection, and pleural effusion, which was relieved by ceftriaxone combined with levofloxacin therapy, the drainage of psoas abscess and pleural effusion. Therefore, since multiple anatomic sites infection, including bloodstream, psoas abscess and pulmonary infection caused by Aggregatibacter aphrophilus, is rare, sufficient attention should be paid to its clinical diagnosis and treatment.

The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges.

腹腔妊娠是指胚胎(或胎儿)位于除输卵管、卵巢及阔韧带以外的腹腔内的妊娠,其发生率为1/15 000～1/30 000,占所有异位妊娠的1％左右[1],但病死率却是非腹腔妊娠的7～8倍[2],故腹腔妊娠罕见且凶险.升结肠表面妊娠是腹腔妊娠的一种,更为罕见.现报告温州医科大学附属东阳医院收治的1例升结肠表面妊娠病例,分析其超声表现及临床诊治经过,以增强临床工作者对腹腔妊娠的认识.

血管母细胞瘤又称毛细血管性血管母细胞瘤、血管网状细胞瘤或毛细胞血管内皮细胞瘤,是由于中胚叶形成的血管细胞的真性肿瘤,好发于小脑,幕上少见,椎管内硬膜外血管母细胞瘤罕见.现报告1例椎管内硬膜外实性血管母细胞瘤,通过回顾文献,结合分析本例影像及临床诊治过程,增强对该肿瘤的认识,为该病的诊治提供参考.