Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.

Background: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

Metal artifacts reduction using monochromatic images from spectral CT: evaluation of pedicle screws in patients with scoliosis.

PURPOSE: To evaluate the effectiveness of spectral CT in reducing metal artifacts caused by pedicle screws in patients with scoliosis. MATERIALS AND METHODS: Institutional review committee approval and written informed consents from patients were obtained. 18 scoliotic patients with a total of 228 pedicle screws who underwent spectral CT imaging were included in this study. Monochromatic image sets with and without the additional metal artifacts reduction software (MARS) correction were generated with photon energy at 65keV and from 70 to 140keV with 10keV interval using the 80kVp and 140kVp projection sets. Polychromatic images corresponded to the conventional 140kVp imaging were also generated from the same scan data as a control group. Both objective evaluation (screw width and quantitative artifacts index measurements) and subjective evaluation (depiction of pedicle screws, surrounding structures and their relationship) were performed. RESULTS: Image quality of monochromatic images in the range from 110 to 140keV (0.97±0.28) was rated superior to the conventional polychromatic images (2.53±0.54) and also better than monochromatic images with lower energy. Images of energy above 100keV also give accurate measurement of the width of screws and relatively low artifacts index. The form of screws was slightly distorted in MARS reconstruction. CONCLUSIONS: Compared to conventional polychromatic images, monochromatic images acquired from dual-energy CT provided superior image quality with much reduced metal artifacts of pedicle screws in patients with scoliosis. Optimal energy range was found between 110 and 140keV.

A pomegranate heart.

Sacrococcygeal teratomas are common in infants while a buttock terotoma is extremely rare in adults. Herein we report a 30-year-old female complaining of left hip swelling and pain for 2 weeks. Ultrasonography demonstrated a subcutaneous cystic mass with multiple dense echo spots inside. MR showed a heart-shaped mass in the left buttock, with multiple "pomegranate seeds" inside, which showed hyperintensity on diffusion weighted imaging. The mass was resected and a buttock terotoma containing yellow sebum-like materials and hairs was confirmed pathologically. A thick walled cystic mass on CT or MR images, which contains multiple small solid nodules or fat, indicates the diagnosis of a terotoma. Timely diagnosis and complete resection bring good prognosis. Adequate follow-up is necessary.