Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer.

OBJECTIVES: To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images. METHODS: A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC. RESULTS: The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01). CONCLUSIONS: CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma. KEY POINTS: • After CT image normalization, more radiomic features were able to be identified. • Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization. • The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various studies on the effects of the vaginal microbiome on PTB, a practical method for its clinical application has yet to be developed. METHOD OF STUDY: In this case-control study, 94 Korean pregnant women with PTB (n = 38) and term birth (TB; n = 56) were enrolled. Their cervicovaginal fluid (CVF) was sampled, and a total of 10 bacteria were analyzed using multiplex quantitative real-time PCR (qPCR). The PTB and TB groups were compared, and a PTB prediction model was created using bacterial risk scores using machine learning techniques (decision tree and support vector machine). The predictive performance of the model was validated using random subsampling. RESULTS: Bacterial risk scoring model showed significant differences (P < 0.001). The PTB risk was low when the Lactobacillus iners ratio was 0.812 or more. In groups with a ratio under 0.812, moderate and high risk was classified as a U. parvum ratio of 4.6 × 10-3 . The sensitivity and specificity of the PTB prediction model using bacteria risk score were 71% and 59%, respectively, and 77% and 67%, respectively, when white blood cell (WBC) data were included. CONCLUSION: Using machine learning, the bacterial risk score in CVF can be used to predict PTB.

Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based parameter selection method.

PURPOSE: Polychromatic x-rays are used in most computed tomography scanners. In this case, a beam-hardening effect occurs, which degrades the image quality and distorts the shapes of objects in the reconstructed images. When the beam-hardening artifact is not severe, conventional correction methods can reduce the artifact reasonably well. However, highly dense materials, such as iron and titanium, can produce more severe beam-hardening artifacts, which often cannot be corrected by conventional methods. Moreover, when the size of the metal is large, severe darks bands due to photon starvation as well as beam-hardening are generated. The purpose of our study was to develop a new method for correcting severe beam-hardening artifacts and severe dark bands using a high-order polynomial correction function and a prior-image-based linearization method. METHODS: The initial estimate of an image free of beam-hardening (a prior image) was constructed from the initial reconstruction of the original projection data. Its corresponding beam-hardening-free projection data (a prior projection) were calculated by a projection operator onto the prior image. A new beam-hardening correction function G(praw ) with many high-order terms was effectively determined via a simple minimization process applied to the difference between the original projection data and the prior projection data. Using the determined correction function G(praw ), a corrected linearized sinogram pcorr can be obtained, which became effectively linear for the line integrals of the object. Final beam-hardening corrected images can be reconstructed from the linearized sinogram. The proposed method was evaluated in both simulation and real experimental studies. RESULTS: All investigated cases in both simulations and real experiments showed that the proposed method effectively removed not only streaks for moderate beam-hardening artifacts but also dark bands for severe beam-hardening artifacts without causing structural and contrast distortion. CONCLUSIONS: The prior-image-based linearization method exhibited better correction performance than conventional methods. Because the proposed method did not require time-consuming iterative reconstruction processes to obtain the optimal correction function, it can expedite the correction procedure and incorporate more high-order terms in the linearization correction function in comparison to the conventional methods.

Small-scale noise-like moiré pattern caused by detector sensitivity inhomogeneity in computed tomography.

We report a new type of moiré pattern caused by inhomogeneous detector sensitivity in computed tomography. Defects in one or a few detector bins or miscalibrated detectors induce well-known ring artifacts. When detector sensitivity is not homogenous over all detector bins, these ring artifacts occur everywhere as distributed rings in reconstructed images and may cause a moiré pattern when combined with insufficient view sampling, which induces a noise-like pattern or a subtle texture in the reconstructed images. Complete correction of the inhomogeneity in detectors can remove the pattern and improve image quality. This paper describes several properties of moiré patterns caused by detector sensitivity inhomogeneity.