Machine learning­based radiomics models accurately predict Crohn's disease­related anorectal cancer.

The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.

Predictive Biomarker for Progression Into the Sunset Glow Fundus of Vogt-Koyanagi-Harada Disease, Using Adaptive Binarization of Fundus Photographs.

Purpose: The sunset glow fundus (SGF) appearance in Vogt-Koyanagi-Harada (VKH) disease was evaluated by means of adaptive binarization of patients' fundus photographs. Methods: Twenty-nine Japanese patients with acute VKH were enrolled in this study. We evaluated one eye of each patient, and thereby divided the patients into two groups; SGF+ and SGF- at 6 months after treatment. We compared patient age, gender, and spherical equivalent refractive error (SERE) and choroidal thickness measured using optical coherence tomography. We also compared the choroidal vascular appearance index (CVAI), derived by adaptive binarization image processing of fundus photographs, between the two groups. Measurements of choroidal thickness and CVAI were taken at the onset of disease, and 1, 3, and 6 months after treatment. The sunset glow index (SGI), as previously reported, was calculated using color fundus photographs, and compared to the CVAI. Results: Eight patients (27.6%) were categorized into the SGF+ group. At all time points, the mean CVAI in the SGF+ group was significantly greater than that in the SGF- group. No significant difference was observed in choroidal thicknesses at any time point. The SGI was significantly greater in the SGF+ group at 6 months. Conclusions: CVAI could be a new predictive biomarker for the development of SGF in patients with VKH disease. Translational Relevance: Detecting SGF is important for management of patients with VKH, and CVAI may indicate the possibility of developing into SGF, although the color fundus photographs do not yet show SGF at that time.

Choroidal thickness estimation from colour fundus photographs by adaptive binarisation and deep learning, according to central serous chorioretinopathy status.

This study was performed to estimate choroidal thickness by fundus photography, based on image processing and deep learning. Colour fundus photography and central choroidal thickness examinations were performed in 200 normal eyes and 200 eyes with central serous chorioretinopathy (CSC). Choroidal thickness under the fovea was measured using optical coherence tomography images. The adaptive binarisation method was used to delineate choroidal vessels within colour fundus photographs. Correlation coefficients were calculated between the choroidal vascular density (defined as the choroidal vasculature appearance index of the binarisation image) and choroidal thickness. The correlations between choroidal vasculature appearance index and choroidal thickness were -0.60 for normal eyes (p < 0.01) and -0.46 for eyes with CSC (p < 0.01). A deep convolutional neural network model was independently created and trained with augmented training data by K-Fold Cross Validation (K = 5). The correlation coefficients between the value predicted from the colour image and the true choroidal thickness were 0.68 for normal eyes (p < 0.01) and 0.48 for eyes with CSC (p < 0.01). Thus, choroidal thickness could be estimated from colour fundus photographs in both normal eyes and eyes with CSC, using imaging analysis and deep learning.