Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

Radiomics-based machine learning role in differential diagnosis between small renal oncocytoma and clear cells carcinoma on contrast-enhanced CT: A pilot study

Purpose

To investigate the potential role of radiomics-based machine learning in differentiating small renal oncocytoma (RO) from clear cells carcinoma (ccRCC) on contrast-enhanced CT (CECT).

Material and methods

Fifty-two patients with small renal masses who underwent CECT before surgery between January 2016 and December 2020 were retrospectively included in the study. At pathology examination 39 ccRCC and 13 RO were identified. All lesions were manually delineated unenhanced (B), arterial (A) and venous (V) phases. Radiomics features were extracted using three different fixed bin widths (bw) of 25 HU, 10 HU, and 5 HU from each phase (B, A, V), and with different combinations (B+A, B+V, B+A+V, A+V), leading to 21 different datasets. Montecarlo Cross Validation technique was used to quantify the estimator performance. The final model built using the hyperparameter selected with Optuna was trained again on the training set and the final performance evaluation was made on the test set.

Results

The A+V bw 10 achieved the greater median (IQR) balanced accuracy considering all the models of 0.70 (0.64-0.75), while A bw 10 considering only the monophasic ones. The A bw 10 model achieved a median (IQR) sensitivity of 0.60 (0.40-0.60), specificity of 0.80 (0.73-0.87), AUC-ROC of 0.77 (0.66-0.84), accuracy of 0.75 (0.70-0.80), and a Phi Coefficient of 0.38 (0.20-0.47). None of the nine models with the lowest mean balanced accuracy values implemented features from A.

Conclusion

The A bw 10 model was identified as the most efficient mono-phasic model in differentiating small RO from ccRCC.

Are Radiomic Spleen Features Useful for Assessing the Response to Infliximab in Patients With Crohn's Disease? A Multicenter Study

INTRODUCTION

To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab.

METHODS

Radiomics features of the spleen were extracted from computed tomography enterography images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator algorithm, and a radiomics score was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the radiomics score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic curves, decision curve analysis curves, and clinical impact curves.

RESULTS

Among the 175 patients with CD, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (area under the curve [AUC] 0.909, 95% confidence interval [CI] 0.840-0.978), the validation cohort (AUC 0.954, 95% CI 0.889-1), and the external cohort (AUC = 0.902, 95% CI 0.83-0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram.

DISCUSSION

Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicenter validation provided high-level evidence for its clinical application.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters

BACKGROUND

To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated.

METHODS

Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12.

RESULTS

The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12.

CONCLUSIONS

CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.