Tall cell carcinoma with reversed polarity (TCCRP) of the breast is a rare malignant neoplasm that shares morphological similarities with the tall-cell variant of papillary thyroid carcinoma, which may lead to diagnostic confusion. Immunohistochemical and molecular analyses are essential to confirm mammary origin and exclude metastatic thyroid carcinoma. Herein, we report a case of a 62-year-old woman, who was diagnosed with TCCRP based on characteristic histomorphological features and immunohistochemical findings in a breast mass. Despite exhibiting a triple-negative immunophenotype, TCCRP is associated with an indolent clinical course. Accurate diagnosis requires integration of morphological, immunohistochemical, and molecular features, which are critical for appropriate classification and may help support a more conservative management approach.

RATIONALE AND OBJECTIVES:This study aimed to assess the predictive value of clinicopathological characteristics, conventional magnetic resonance imaging (MRI), intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) parameters for hypoxia-inducible factor-1α (HIF-1α) expression in breast cancer. MATERIALS AND METHODS:We retrospectively enrolled 146 breast cancer patients receiving preoperative multiparametric MRI and surgery from 2019 to 2023, who were randomly assigned into training (n = 103) and validation (n = 43) cohorts at 7:3 ratio. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were conducted, and a nomogram was constructed based on independent predictive factors. RESULTS:The high-expression group had a higher proportion of axillary lymph node metastasis (ALN_metastasis), advanced histological grades, unclear margin, time intensity curve (TIC)-III type, lower D values, and higher Ktrans and Kep values compared with the low-expression group (P < 0.05). The area under the curves(AUCs) for the pathological, conventional MRI, IVIM-DWI, DCE-MRI, and combined models (ALN_metastasis + TIC type + D + Kep) were 0.765, 0.732, 0.771, 0.804, and 0.958 in the training cohort, respectively. The combined model significantly outperformed individual models (combined model vs. conventional MRI model, Z = 4.890, P < 0.001; combined model vs. pathological model, Z = 4.429, P < 0.001; combined model vs. IVIM model, Z = 3.724, P < 0.001; combined model vs. DCE-MRI model, Z = 3.691, P < 0.001). CONCLUSION:The nomogram combining clinicopathological and multimodal MRI parameters can accurately predict HIF-1α expression non-invasively and assist personalized breast cancer therapy.

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, potentially improving cancer control and clinical outcomes. In this study, we aimed to develop and validate a machine learning model integrating radiomics, dosiomics, and clinical characteristics to predict response to radiotherapy in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC). A total of 222 patients across multiple centers who received radiotherapy for NSCLC were enrolled and divided into training (n = 110), internal validation (n = 28), and external validation (n = 84) cohorts. Objective response rate (ORR) and progression-free survival (PFS) were predicted using models based on radiomics, dosiomics, and clinical characteristics. Model performance was evaluated using receiver operating characteristic (ROC) curves, DeLong test, decision curve analysis (DCA), Kaplan-Meier survival analysis, and Integrated Brier Score (IBS). The clinical models, CORR and CPFS (both based on planning target volume [PTV] and lymphocyte count) were compared with combined radiomics-dosiomics-clinical models (RDCORR and RDCPFS). For ORR prediction, RDCORR achieved AUCs of 0.901, 0.894, and 0.869 in the training, internal validation, and external validation cohorts, outperforming CORR (AUCs of 0.723, 0.606, and 0.723), with p < 0.05. DCA indicated that RDCORR outperformed CORR, providing a higher overall net benefit. For PFS prediction, RDCPFS yielded higher concordance indices (0.805, 0.730, and 0.743 in the training, internal validation, and external validation cohorts) than CPFS (0.679, 0.699, and 0.640, respectively). RDCPFS showed the lowest IBS across all cohorts (0.092, 0.107, and 0.093, respectively) compared with CPFS and the reference model, indicating better predictive accuracy. The combined model integrating radiomics, dosiomics, and clinical characteristics enhances the prediction of radiotherapy response in locally advanced, unresectable NSCLC, facilitating improved patient monitoring and more effective adjuvant clinical trial design.

PURPOSE:To develop and validate a combined model integrating multimodal magnetic resonance imaging (MRI) and clinical-pathological parameters to assess hypoxia status and hypoxia-inducible factor-1α (HIF-1α) expression in mass-like breast cancer. METHODS:This retrospective cohort study included 197 patients with mass-like breast cancer from two medical centers, who were divided into a training set (n = 104), an internal validation set (n = 45), and an external validation set (n = 48). Clinical-pathological and multimodal MRI parameters were analyzed using histopathology as the reference. The combined model was developed through logistic and least absolute shrinkage and selection operator (LASSO) regression analysis to identify features, and visualized using nomograms. RESULTS:Axillary lymph node (ALN) metastasis, time-intensity curve (TIC) type, mean diffusivity (MD), volumetric transfer constant (Ktrans), and rate constant (Kep) were selected to construct the combined model. The diagnostic performance of the combined model [area under the curve (AUC) = 0.967, 95 % CI: 0.913-0.992] (training), was significantly better than that of other individual models (P < 0.001). This performance was replicated in internal (AUC= 0.907) and external (AUC = 0.928) validation sets. The nomogram of the combined model showed excellent calibration (Hosmer-Lemeshow P = 0.653) and the highest net benefit across threshold probabilities in the decision curve analysis (DCA). Tumors with high HIF-1α expression exhibited higher ALN metastasis, histological grade, unclear margins, Type 3 TIC, and elevated Ktrans, Kep, and MK, but reduced MD values. CONCLUSIONS:The combined model (ALN + TIC + MD + Ktrans + Kep) shows potential as a reliable tool for predicting HIF-1α expression levels in mass-like breast cancer.

Purpose:The research aims to explore the predictive significance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and their integrated models in relation to Hypoxia-inducible factor-1 alpha (HIF-1α), Ki-67, and vascular endothelial growth factor (VEGF) expression levels in breast carcinoma. Materials and Methods:This retrospective study included 104 patients with pathologically confirmed breast carcinoma from our institution as the training set, while an external validation cohort of 91 eligible patients was recruited from another tertiary medical center. Two independently working radiologists analyzed IVIM-derived parameters apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f), and DKI-derived parameters mean diffusivity (MD) and mean kurtosis (MK). Receiver operating characteristic (ROC) curves were constructed for evaluation of diagnostic efficacy. The outcomes of the multivariate logistic regression model were employed to create a nomogram of the combined model for molecular marker status prediction. Results:High expression levels of HIF-1α, VEGF, and Ki-67 were consistently associated with lower D, MD, and ADC values, and higher perfusion-related D*, f, and MK values (all P<0.05). ROC curve analysis showed that among the individual parameters, the D value exhibited the highest predictive efficacy (Area Under the Curve, AUC = 0.724). A D value ≤ 0.88×10-3 mm2/s should strongly suggest high HIF-1α expression. ROC curve analysis revealed that the f parameter was the most powerful single indicator for predicting VEGF expression (AUC = 0.882). In clinical practice, an f value ≥ 29.82% can serve as a key imaging biomarker suggesting high VEGF expression, i.e., active tumor angiogenesis. ROC curve analysis indicated MD as the most predictive single parameter for Ki-67 expression (AUC = 0.762), showing significantly greater efficacy than D* (Z = 2.022, P = 0.043). Thus, an MD value ≤ 2.21×10-3 mm2/s strongly suggests high tumor proliferative activity. In the training set, the combined models integrating select parameters from IVIM and DKI showed significantly higher predictive performance (AUCs: 0.852-0.923) compared to individual parameters. This performance was replicated in the external validation set (AUCs: 0.841-0.918), with no statistically significant difference in AUCs between the training and external validation sets according to DeLong's test (all P > 0.05). Moreover, the solid line provided a better approximation of the ideal dotted line, indicating higher predictive accuracy of the nomograms (P = 0.59, 0.40, and 0.08). According to the decision curve analysis (DCA), the predictive model provided a substantial net clinical benefit. Conclusion:Our findings suggest that IVIM may be usefully combined with DKI to help predict the expression levels of Ki-67, HIF-1α, and VEGF in breast cancer, generating hypotheses for future research. Furthermore, the diagnostic efficiency of the parameters D* and f appears to be enhanced by employing more low b-values (<100-200 s/mm²). These results require confirmation in prospective, multi-center studies.

RATIONALE AND OBJECTIVES:To investigate the value of clinicopathological features and multiparametric magnetic resonance imaging (MRI) in predicting tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS AND METHODS:A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results. RESULTS:Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05). CONCLUSION:Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.

Background:Detection of metastases in axillary lymph nodes (ALNs) is of vital significance for determining appropriate therapeutic strategies and prognosis for breast cancer patients. Studies combining multiparametric magnetic resonance imaging (MRI) and pathological biomarkers for predicting ALN metastasis in breast cancer are rarely reported. This study aimed to evaluate the predictive value of conventional MRI features, intravoxel incoherent motion (IVIM), quantitative dynamic contrast-enhanced MRI (DCE-MRI), and pathological biomarkers for ALN metastasis in breast cancer patients. Methods:In total, 149 subjects with breast cancer confirmed via pathology were recruited for study. Among the participants, patients were randomly allocated to the training cohort (42 and 62 presented with ALN and non-ALN metastasis) or validation cohort (18 and 27 presented with ALN and non-ALN metastasis), respectively. All participants underwent both IVIM and DCE-MRI. The analysis focused on the clinicopathological characteristics along with conventional MRI features, in addition to assessment of a range of quantitative parameters, including DCE-MRI derived parameters (Ktrans, Kep and Ve), and the IVIM-derived parameter [apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), perfusion fraction (f)]. To evaluate diagnostic efficacy in predicting ALN metastasis, multivariate logistic regression and receiver operating characteristic (ROC) curve assessments were conducted. A nomogram for the combined model was created on the basis of the findings derived from the multivariate logistic regression model. Results:In the training and validation cohorts, patients with ALN metastasis had significantly higher Ki-67 (P=0.01, P=0.03) and hypoxia-inducible factor-1 alpha (HIF-1α) expression (P<0.001, P=0.04). Lymphovascular invasion (LVI) and programmed death ligand-1 (PD-L1) expression were significantly more common in the metastatic group (P=0.002, P=0.003, respectively) in the training cohort. In the training and test cohorts, compared to the non-metastatic group, patients with ALN metastasis exhibited significantly lower D values (all P<0.001) and significantly higher values of D* (P=0.02, P=0.04), Ktrans (all P<0.001), and Kep (all P<0.001). Multivariate analysis identified PD-L1 [odds ratio (OR) =82.55, P=0.045], lesion margin (OR =21.08, P=0.048), D (OR <1,000, P=0.01), and Ktrans (OR >1,000, P=0.01) as independent predictors. Calibration curves confirmed excellent agreement between predicted and observed outcomes (P=0.99). Furthermore, in both training and test validations, the combined model achieved significantly enhanced the areas under the ROC curve (AUCs) compared with the pathologic, conventional MRI, IVIM, and DCE-MRI models (Z=2.083-4.402, P<0.05). Conclusions:Combining MRI parameters (lesion margin, D, Ktrans) with pathological biomarker PD-L1 significantly improves prediction accuracy for ALN metastasis in breast cancer. This integrated model has considerable clinical potential, enabling precise preoperative assessment and potentially reducing unnecessary lymph node biopsies.

Objective: To explore the value of intravoxel incoherent motion (IVIM) and dynamic contrast enhanced MRI (DCE-MRI) for predicting phenotypic subtypes and Nottingham prognostic index (NPI) of breast cancer. Study Design: Descriptive study. Place and Duration of the Study: Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China, from March 2020 to January 2022. Methodology: One hundred and forty-one breast cancer patients with preoperative IVIM and DCE imaging were collected. IVIM parameters of D, D*, f, and DCE-MRI parameters of K-trans, Kep, and Ve were measured. Receiver operating characteristic curves were conducted to assess the diagnostic efficacies. Additionally, 40 patients collected from February 2022 to July 2022 were enrolled as validation cohort. Results: The D value in HER2-enriched (HER2-E) was lower than that in non -HER -E, while D*, K-trans, and Ve values were higher than that in non -HER -E (p < 0.001, 0.046, < 0.001, and < 0.001, respectively). D + K-trans + Ve showed an optimal diagnostic efficiency (AUC = 0.868). Meanwhile, D* and f values of triple -negative breast cancer (TNBC) were higher than those of non-TNBC, and Ve value of TNBC was lower than that of non-TNBC (p = 0.013, 0.006, and < 0.001, respectively). D* + f+ Ve showed the best prediction performance (AUC = 0.849). Additionally, D and Kep were independent predictors of NPI (p < 0.001, and 0.002, respectively). D + Kep showed a good diagnostic efficiency (AUC = 0.818). Conclusion: The combined IVIM and DCE-MRI model showed enhanced diagnostic efficiency in predicting phenotypic subtypes and NPI of breast cancer, and might thus be considered efficient in therapy decision -making for patients.

BackgroundThe angiographic features of moyamoya disease (MMD) and atherosclerosis‐associated moyamoya vasculopathy (AS‐MMV) are similar, but the etiology and clinical treatment strategies are different. Differentiating MMD from AS‐MMV helps to choose the appropriate treatment.PurposeTo investigate the feasibility of a nomogram based on high‐resolution vessel wall (HR‐VWI) MRI features to differentiate MMD from AS‐MMV.Study TypeRetrospective.SubjectsOne hundred and two patients with MMD (N = 52) or AS‐MMV (N = 50) in the training cohort (9–72 years; 54 females) and 70 patients with MMD (N = 42) or AS‐MMV (N = 28) in the validation cohort (7–69 years; 33 females).Field Strength/Sequence3‐T, three‐dimensional time‐of‐flight MR angiography (3D‐TOF‐MRA), spin echo high‐resolution 3D T1‐weighted imaging (3D‐T1WI), 3D T2‐weighted imaging (3D‐T2WI), and contrast‐enhanced 3D‐T1WI.AssessmentImage assessment was performed by three neuroradiologists (with 10, 15, and 18 years of experience). Demographic characteristic and image features were evaluated and compared. Independent factors of MMD were screened to construct a nomogram model in the training cohort. The validation cohort was used to validated its generality.Statistical TestsInterclass correlation coefficient (ICC), kappa, t‐test, χ2 test, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve and concordance index (C‐index). A P‐value <0.05 was considered statistically significant.ResultsSignificant differences were observed between MMD and AS‐MMV in terms of age, vessel outer diameter, vessel wall thickening pattern, maximum thickness, dot sign, and anterior cerebral artery (ACA) involved. Age, outer diameter, dot sign, and ACA involved were independent factors. The C‐index was 0.886 in the training cohort and 0.859 in the validation cohort. The ROC demonstrated high diagnostic efficacy with an AUC of 0.884 in the training cohort and 0.857 in the validation cohort.Data ConclusionA nomogram model based on age, vessel outer diameter, dot sign and ACA involved may effectively distinguish MMD from AS‐MMV with good reliability and accuracy.Evidence Level4Technical EfficacyStage 2

目的 探讨胃神经鞘瘤(GS)的CT表现特征,以提高对该病的认识.方法 选取33例经手术病理证实的GS患者的临床和CT资料,评估肿瘤的位置、大小、形态、边缘、生长方式、强化方式及程度、肿瘤被覆黏膜面有无溃疡、肿瘤内部有无囊变坏死、钙化、瘤周淋巴结情况及患者临床症状;按肿瘤最大径(Dmax,≥5 cm和＜5 cm)分成两组并对其CT征象进行统计学分析.结果 33例GS均为单发,以胃体大弯侧多见,占60.6％(20/33),以腔外生长为主,占51.5％(17/33),肿瘤Dmax为(4.39±2.01)cm(≥5 cm者12例、＜5 cm者21例).27例肿瘤呈类圆形或卵圆形,6例呈不规则形.32例肿瘤边缘清晰,1例边缘稍模糊.10例肿瘤黏膜面可见溃疡形成,5例瘤内可见囊变坏死、6例瘤内可见钙化.25例肿瘤周围可见多发大小不等的淋巴结,多数呈明显均匀强化,部分不规则淋巴结可见淋巴门结构.27例肿瘤行增强扫描均呈渐进性强化,其中15例呈轻中度强化,6例呈明显强化.两组间肿瘤的形态和瘤内囊变坏死差异有统计学意义(P＜0.05),而两组间年龄、性别、症状、位置、生长方式、表面溃疡、钙化和瘤周淋巴结情况差异无统计学意义(P＞0.05).结论 GS的CT表现具有一定的特征性,其中肿瘤呈轻中度均匀渐进性强化和瘤周伴有多发大小不等明显强化的淋巴结最具有提示作用.

目的 探讨采用宽体探测器CT行全脑灌注联合头颈部CTA一站式扫描的价值.方法 选取接受一站式全脑灌注联合头颈CTA模式扫描的40 例患者作为A组,全脑CT灌注扫描的40 例患者作为B组,对两组患者图像质量进行主观评分及客观评价,比较两种扫描模式下的CT灌注图像质量、CTA图像质量及辐射剂量等.结果 两组CTA图像质量均满足诊断要求,且主观评分差异无统计学意义(P>0.05);客观参数评价中A组颈内动脉末端CT值SI血管(584.88±142.11)HU高于B组(447.93±85.14)HU,差异有统计学意义(P<0.05),其余指标噪声(SD)、信噪比(SNR)、对比噪声比(CNR)差异均无统计学意义(P>0.05).两组CT灌注图像质量均满足诊断要求,其中A组脑血容量(CBV)图像质量略低于B组,差异有统计学意义(P<0.05),其余脑血流量(CBF)、平均通过时间(MTT)、血流峰值时间(Tmax)图像质量主观评分差异无统计学意义(P>0.05).A组辐射剂量、CT剂量指数低于B组,差异有统计学意义(P<0.05).结论 宽体探测器用于一站式全脑CT灌注成像联合头颈CTA扫描方式具有一定的优势,能够保证图像质量满足诊断要求,并且可以降低辐射剂量、缩短扫描时间.

The effectiveness of surgical interventions, whether direct or indirect, for Moyamoya disease (MMD) remains controversial. This study aims to investigate CT perfusion (CTP) as an objective method to evaluate the outcomes of different surgical modalities for adult MMD. The clinical and imaging data of 41 patients who underwent superficial temporal artery-middle cerebral artery (STA-MCA) bypass and 43 who received encephaloduroarteriosynangiosis (EDAS) were retrospectively analyzed. Intra- and intergroup differences in the Modified Rankin Scale (mRS) score, the change in clinical symptoms, collateral grade, and CTP parameters pre- and postoperatively were compared. The overall level of the change in clinical symptoms in the STA-MCA group was higher than in the EDAS group (p < 0.05). In the operative area, the relative cerebral blood flow (rCBF) was significantly higher whereas the relative time to peak (rTTP) and the relative mean transit time (rMTT) were significantly lower in the STA-MCA and EDAS groups postoperatively than preoperatively (all p < 0.05). In the ipsilateral frontal lobe and basal ganglia, the postoperative rCBF was significantly higher, and the rTTP was significantly lower than the preoperative in the STA-MCA group (all p < 0.05). The postoperative rCBF improvement was higher in each brain area for STA-MCA than in the EDAS group (all p < 0.05). Highlighting the utility of CTP, this study demonstrates its effectiveness in assessing postoperative cerebral hemodynamic changes in adult MMD patients. STA-MCA yielded a larger postoperative perfusion area and greater improvement compared to EDAS, suggesting CTP’s potential to elucidate symptom variation between two surgical revascularization procedures. We analyzed computed tomography perfusion parameters in pre- and postoperative adult Moyamoya disease patients undergoing superficial temporal artery-middle cerebral artery bypass and encephaloduroarteriosynangiosis. Our findings suggest computed tomography perfusion’s potential in objectively elucidating symptom variations between these surgical revascularization approaches for MMD. • Postoperative perfusion improvement is only confined to the operative area after EDAS. • Besides the operative area, postoperative perfusion in the ipsilateral frontal lobe and basal ganglia was also improved after STA-MCA. • The degree of perfusion improvement in each brain area in the STA-MCA group was generally greater than that in the EDAS group.

患者 男,56岁.因"1月前无明显诱因腰部疼痛"就诊.查体无异常.胸部CT显示前纵隔内见不规则软组织肿块影,边界欠清,内密度欠均匀,呈分叶状,平扫CT值约40 HU(图1).增强扫描后肿块呈不均匀强化,动静脉期CT值分别约50HU、58 HU(图2,3).诊断前纵隔占位性病变:考虑恶性肿瘤性病变,淋巴瘤可能性大.全腹CT显示肝内多处斑片状稍低密度影,边界欠清,平扫CT值约38 HU(图4).

[This corrects the article DOI: 10.3389/fcvm.2023.1159576.].

目的 探讨基于CT影像特征的列线图鉴别甲状腺良恶性结节的诊断价值.方法 回顾性分析经手术病理证实的262 例甲状腺结节患者的临床和影像资料,其中良性组 114 例,恶性组 148 例,比较两组患者的临床资料及CT影像特征,包括结节的数目、位置、形态、边界、成分、钙化、包膜是否完整、纵横比(轴位及冠状位)、强化方式及平扫、动脉期、静脉期CT值,采用多因素Logistic回归分析筛选鉴别甲状腺良恶性结节的独立影响因素并构建列线图模型,采用受试者工作特征曲线(ROC)、校准曲线验证该模型的性能.结果 甲状腺良恶性结节均好发于女性,良性结节组占比略高于恶性结节组,差异无统计学意义(P>0.05);但良性结节组的平均年龄大于恶性结节组,且差异有统计学意义(P<0.05).良性结节组多位于腺体内,形态较规则,边界清晰,包膜完整;恶性结节组多位于包膜下,形态不规则,边界模糊,包膜不完整.两组结节的位置、形态、边界、成分、钙化、包膜是否完整、轴位纵横比及动脉期CT值、静脉期CT值差异有统计学意义(P<0.05);两组结节的数目、强化方式、冠状位纵横比及平扫CT值差异无统计学意义(P>0.05).将上述差异有统计学意义的CT影像特征纳入多因素Logistic回归分析,筛选出结节位置、边界、轴位纵横比为鉴别甲状腺良恶性结节的独立影响因素[OR值(95%CI)分别为11.266(5.679～22.350),4.414(2.361～8.252),5.348(1.762～16.228)],其中轴位纵横比诊断恶性结节的最佳阈值为纵横比>1.06.根据结果建立列线图模型,模型的曲线下面积(AUC)为0.851(95%CI:0.805～0.889),敏感度为77.5%、特异度为86.7%,校准曲线显示预测概率与实际概率拟合度良好.结论 CT影像特征构建的列线图模型有助于鉴别甲状腺良恶性结节;当甲状腺结节边界模糊、位于包膜下、结节轴位纵横比>1.06 时倾向于恶性结节,反之则倾向于良性结节.

BACKGROUND:Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia.METHODS:In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared.RESULTS:A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05).CONCLUSIONS:The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.

Quadricuspid aortic valve (QAV) and sinus of Valsalva aneurysm (SVA) are rare congenital anomalies. We report an elderly patient with QAV associated with a ruptured SVA to the right atrium. Transthoracic echocardiographic and computed tomographic images are presented. We emphasize the important role of computed tomography angiography in establishing and confirming the diagnosis and facilitating treatment planning. The patient was successfully operated by a minimally invasive approach.