Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling.

OBJECTIVES: To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS: A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS: The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION: Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS: • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

Basilar artery fenestration detected with CT angiography.

OBJECTIVES: To analyse the characteristics of basilar artery (BA) fenestrations and their coexistence with aneurysms and other anomalies in a massive cases by computed tomographic angiography (CTA). METHODS: A total of 5,657 sequential cerebral CTA images performed from January 2006 to February 2012 were reviewed. CTA images were obtained from the raw datasets by using volume rendering and maximal intensity projection reconstruction. RESULTS: One hundred and thirty-two (2.33 %) BA fenestrations were detected with CTA, and most common at the proximal segment (n = 124). BA fenestration-associated aneurysms were found in 34 cases and 7 located at the posterior circulation, and the frequency of posterior circulation aneurysms was significantly different in patients with and without BA fenestrations (P = 0.025). Other associated anomalies included arteriovenous malformation (n = 7) and moyamoya disease (n = 6). BA fenestrations were classified into Type I (74 cases), Type II (15 cases), Type III (41 cases) and Type IV (2 cases). A significant difference was observed between Types II + III associated with convex-lens-like and slit-like fenestrations (P = 0.008). CONCLUSIONS: BA fenestrations were found in 2.33 % with CTA. They were significantly more often associated with posterior circulation aneurysms than those without BA fenestration. The anterior inferior cerebral artery (AICA) tends to originate more often from convex-lens-like fenestration than slit-like. KEY POINTS: • Basilar artery fenestrations were found in 2.33 % of patients undergoing CT angiography. • Fenestrations were seen more often in the lower third with slit-like configurations. • No obvious relationship exists between basilar artery fenestration and aneurysm formation. • Basilar artery fenestrations perhaps predispose a patient to posterior circulation aneurysm formation. • The AICA tends to originate more often from convex-lens-like than slit-like fenestrations.

Effect of systolic blood pressure fluctuations during resuscitation on postoperative complications following meningioma surgery: A retrospective observation study.

It is unclear whether blood pressure variability in the post-anesthesia care unit is associated with postoperative complications. This study aims to characterize the impact of blood pressure fluctuations on postoperative complications and postoperative length of stay after meningioma surgery. Adult meningioma patients undergoing general anesthesia were retrospectively recruited. The principal exposure was blood pressure variability in the post-anesthesia care unit, calculated by noninvasive blood pressure measurements. The primary outcome was major postoperative complications, defined as II or higher in the Clavien-Dindo classification grades. Secondary outcomes included healthcare resource utilization parameters among patients. Multivariable logistic regression was used and adjusted for potential confounding variables. Data sensitivity analyses were performed via different variable transformations and propensity score matching analyses. A total of 578 patients qualified for the study, and 161 (27.9%) cases experienced postoperative complications. The multivariable analysis found that increased systolic blood pressure variability in the post-anesthesia care unit was associated with postoperative complications (adjusted odds ratio [aOR] = 1.15; 95% confidence interval [CI], 1.09-1.22, P < .001) and prolonged postoperative length of stay (adjusted regression coefficients [ß] = 1.86; 95% CI, 0.58-3.13, P = .004). Patients with postoperative complications had a higher frequency of intensive care admission (44.1% vs 15.3%), major postoperative interventions (6.6% vs 0%), and 30-day readmission (5.0% vs 0.7%). Systolic blood pressure fluctuations during resuscitation have an independent impact on postoperative complications and postoperative length of stay following meningioma surgery.

A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry.

The accurate identification of unknown illegal additive compounds in complex health foods continues to be a challenging task in routine analysis, because massive false positive results can be screened with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry-based untargeted techniques and must be manually filtered out. To address this problem, we developed a chemometric-based strategy, in which data analysis was first performed by using XCMS, MS-DIAL, Mzmine2, and AntDAS2, to select those that provided acceptable results to extract common features (CFs), which can be detected by all of the selected methods. Then, CFs whose contents were significantly higher in the suspected illegal additive group were screened. Isotopic, adduct, and neutral loss ions were marked based on the CFs by using a new adaptive ion annotation algorithm. Fragment ions originating from the same compound were identified by using a novel fragment ion identification algorithm. Finally, a respective mass spectrum was constructed for each screened compound to benefit compound identification. The developed strategy was confirmed by using a complex Chinese health food, Goujiya tea. The features of all illegal additive compounds were precisely screened by the developed strategy, and massive false positive features from the current data analysis method were greatly reduced. The constructed respective mass spectra can benefit compound identification and avoid the risk of identifying ions from the same illegal compound as different compounds. Moreover, unknown compounds that are contained in an illegal compound library can be screened.