Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm.

Objective: In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm. Methods: A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085). Conclusion: the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

The diagnostic performance of dual-layer spectral detector CT for distinguishing breast cancer biomarker expression and molecular subtypes.

To evaluate the diagnostic performance of dual-layer spectral detector CT for differentiation of breast cancer molecular subtypes. This study was done in a retrospective approach including 104 female patients histopathologically proven to have breast cancer. These patients underwent chest arterial and venous phase dual-layer SDCT. CT values, iodine concentrations (IC)s, and Z-effective (Zeff) values of the lesions and arteries in the same layer were determined for both arterial and venous phases. Parameter values were normalized, and slopes of the spectral curves (λHu) were calculated. Breast cancer biomarkers were also analyzed. Afterward, correlations between the obtained parameters and biomarkers were analyzed. Eventually, the diagnostic performance was assessed using ROC curves. ER or PR-negative patients generally showed significantly higher mean iodine concentrations, CT, and Z-effective values. HER2-positive patients showed significantly higher CTVE, ZeffVE, N-ZeffVE, ICART, ICVE, NICART, NICVE, and λVE. Only ICVE and ZeffVE differed significantly between Ki67-positive and negative patients. All parameters showed significant diagnostic value for subtypes except N-ZeffART. Luminal and non-luminal types differed significantly and ROC curves indicated that multi-factors had the best diagnostic efficacy. The dual-layer SDCT distinguishes breast cancer biomarker expression and molecular subtypes. Thus, it can be used for preoperative assessment of breast cancer.

Assessment of early anthracycline-induced cardiotoxicity and liver injury with T2 and T2* mapping in rabbit models.

OBJECTIVES: To evaluate the early prevalence of anthracycline-induced cardiotoxicity (AIC) and anthracycline-induced liver injury (AILI) using T2 and T2* mapping and to explore their correlations. MATERIALS AND METHODS: The study included 17 cardiotoxic rabbits that received weekly injections of doxorubicin and magnetic resonance imaging (MRI) every 2 weeks for 10 weeks. Cardiac function and T2 and T2* values were measured on each period. Histopathological examinations for two to five rabbits were performed after each MRI scan. The earliest sensitive time and the threshold of MRI parameters for detecting AIC and AILI based on these MRI parameters were obtained. Moreover, the relationship between myocardial and liver damage was assessed. RESULTS: Early AIC could be detected by T2 mapping as early as the second week and focused on the 7th, 11th, and 12th segments of left ventricle. The cutoff value of 46.64 for the 7th segment had the best diagnostic value, with an area under the curve (of 0.767, sensitivity of 100%, and specificity of 52%. T2* mapping could detect the change in iron content for early AIC at the middle interventricular septum and AILI as early as the sixth week (p = 0.014, p = 0.027). The T2* values of the middle interventricular septum showed a significant positive association with the T2* values of the liver (r = 0.39, p = 0.002). CONCLUSION: T2 and T2* mapping showed value one-stop assessment of AIC and AILI and could obtain the earliest MRI diagnosis point and optimal parameter thresholds for these conditions. CLINICAL RELEVANCE STATEMENT: Anthracycline-induced cardiotoxicity could be detected by T2 mapping as earlier as the second week, mainly focusing on the 7th, 11th, and 12th segments of left ventricle. Combined with T2* mapping, hepatoxicity and supplementary cardiotoxicity were assessed by one-stop scan. KEY POINTS: • MRI screening time of cardiotoxicity was as early as the second week with focusing on T2 values of the 7th, 11th, and 12th segments of left ventricle. • T2* mapping could be used as a complement to T2 mapping to evaluate cardiotoxicity and as an effective index to detect iron change in the early stages of chemotherapy. • The T2* values of the middle interventricular septum showed a significant positive association with the T2* values of the liver, indicating that iron content in the liver and heart increased with an increase in the chemotherapeutic drugs.

Assessment of early anthracycline-induced cardiotoxicity using segmental strain of cardiac magnetic resonance compared with global strain and functional parameters: an animal study.

Background: The identification of anthracycline-induced cardiotoxicity holds significant importance in guiding subsequent treatment strategies, and recent research has demonstrated the efficacy of cardiac magnetic resonance (CMR) global strain analysis for its diagnosis. On the other hand, it is noteworthy that abnormal global myocardial strain may exhibit a temporal delay due to different cardiac movement in each segment of the left ventricle. To address this concern, this study aims to assess the diagnostic utility of CMR segmental strain analysis as an early detection method for cardiotoxicity. Methods: A serials of CMR scans were performed in 18 adult males New Zealand rabbits at baseline time (n=15), followed by scans at week 2 (n=15), week 4 (n=9), week 6 (n=6), and week 8 (n=5) after each week's anthracycline injection. Additionally, following each CMR scan, two to three rabbits were euthanized for pathological comparison. Cardiac functional parameters, global peak strain parameters, segmental peak strain parameters of the left ventricle, and the presence of myocardial cells damage were obtained. A mixed linear model was employed to obtain the earliest CMR diagnostic time. Receiver operating characteristic (ROC) analysis was performed to get the parameter threshold indicative of cardiotoxicity. Results: The left ventricular ejection fraction decreased at week 8 (P=0.002). There were no statistical differences in global strain throughout the experiment period (P>0.05). Regarding segmental strain analysis, the peak segmental radial strain of the apical lateral wall exhibited a decrease starting from week 2 and reached its lowest point at this week (P=0.011). Conversely, peak segmental circumferential strain of the apical anterior wall showed an increase at week 2 and reached its peak at week 6 (P=0.026). The cutoff strain value by ROC analysis for these two walls were 46.285 and -16.920, with the respective areas under the curve (AUC) 0.593 [specificity =0.267, sensitivity =1.000, 95% confidence interval (CI): 0.471-0.777] and 0.764 (specificity =0.733, sensitivity =0.784, 95% CI: 0.511-0.816). Peak segmental longitudinal strain of the apical anterior and apical lateral wall showed relatively delayed changes, occurring in the 4th week (P=0.030 and P=0.048), the cutoff values for these strains were -12.415 and -15.960, with corresponding AUCs of 0.645 (specificity =0.333, sensitivity =0.955, 95% CI: 0.495-0.795) and 0.717 (specificity =0.433, sensitivity =0.955, 95% CI: 0.566-0.902), respectively. Notably, the myocardial injury was also observed at the corresponding periods. Conclusions: Based on experimental evidence, the peak segmental strain of the apical lateral and anterior wall, as determined by CMR, demonstrated an earlier detection of anthracycline-induced cardiotoxicity compared to peak global strain and cardiac function.

A meta-analysis on the diagnostic value of diffusion-weighted imaging on ovarian cancer.

PURPOSE: This study systematically evaluated the potential influences of diffusion- weighted imaging (DWI) on the initial diagnosis, clinical decision making and diagnostic accuracy of ovarian cancer in the follow-up period. METHODS: Literature on the correlation between DWI and diagnosis of ovarian cancer were searched from PubMed, Embase, Cochrane Library, and Web of Science published before January 1, 2019. References in enrolled eligible literature were manually reviewed. Quality assessment on the diagnostic accuracy was performed using the QUADAS scale. Receiver operating characteristics (ROC) curve was depicted using STATA 12.0. Study heterogeneity and its sources were determined. Sensitivity (SEN), specificity (SPF), positive likelihood ratio (+LR), negative likelihood ratio (-LR) and diagnostic odds ratio (DOR) of eligible studies were calculated for depicting forest plot and summary of ROC curve (SROC). The area under the curve (AUC) was calculated. RESULTS: A total of 15 articles involving 930 ovarian cancer cases and 832 control cases were enrolled. DWI was identified to exert a certain diagnostic value on ovarian cancer. The 95%CI of the merged SEN (91%, 95%CI=84-95%), SPF (85%, 95%CI=78-90%), +LR (6.18, 95%CI=4.17-9.15) and -LR (4.05, 95%CI=3.30-4.79) were calculated using the random-effects model due to the slight heterogeneity among these studies. AUC was 0.94 (95%CI=0.91-0.96). Subgroup analysis in Asian population obtained the following results: SEN was 85% (95%CI=78-91%), SPF 83% (95%CI=72-90%), +LR 0.18 (95%CI=0.11-0.27), -LR 3.34 (95%CI=2.60-4.09) and DOR 3.34 (95%CI=2.60-4.09); AUC was 0.91 (95%CI=0.88-0.93). In Caucasian population, SEN was 96% (95%CI=83-99%), SPF 89% (95%CI=84-93%), +LR 41.36 (95%CI=5.95-287.48), -LR 0.06 (95%CI=0.02-0.18) and DOR 5.31(95%CI=3.93-6.69); AUC was 0.94 (95%CI=0.91-0.96). CONCLUSIONS: This meta-analysis proved that DWI exerted a relatively high sensitivity and specificity in diagnosing ovarian cancer, especially in the Caucasian population. This conclusion still needs to be further verified in a multi-center study with a large sample size.