[Relation between corneal biomechanical alteration after small incision lenticule extraction and intraoperative cutting thickness].

Objective: To explore the rule of corneal biomechanical alteration with residual stromal thickness (RST) and percent tissue altered (PTA) after small incision lenticule extraction (SMILE) and to determine the factors influencing postoperative corneal biomechanical properties. Methods: In this retrospective study, a total of 184 patients (184 right eyes) who underwent SMILE in Tianjin Eye Hospital Refractive Surgery Center from January 2019 to January 2020 were enrolled. There were 83 males and 101 females with age of (24.6±5.8) years. Corneal biomechanical parameters, including DA ratio, stiffness parameter at the first applanation (SPA1) and integrated radius (IR), were measured with Corvis ST preoperatively and at 3 months postoperatively. The association between PTA, RST and the changes of DA ratio, SPA1 and IR was assessed by linear and nonlinear regression analyses. Stepwise multivariate regression analyses were conducted to explore the factors associated with postoperative corneal biomechanical parameters with age, sex, anterior mean keratometry, spherical equivalent, postoperative central corneal thickness (CCT) and preoperative corneal biomechanical parameters as covariates. Preoperative and postoperative data were compared using the paired t test. Correlations were determined by the Pearson or Spearman analysis. Results: The alterations at 3 months postoperatively of DA ratio, SPA1 and IR were 1.33 (30.0%), 28.05 (26.0%) and 2.56 (34.0%), respectively. The changes before and after surgery were statistically significant (t=35.52, -28.00, 36.95, P<0.01). The best-fit curve showed that the changes of DA ratio, SPA1 and IR increased with the decrease of RST or increase of PTA. When the RST was<280 µm or the PTA was>28%, the slope of the change of DA ratio curve was significantly increased. Multivariate regression models showed that the factors with the greatest influence on postoperative DA ratio, SPA1 and IR were preoperative DA ratio (Sß=0.489, P<0.01), preoperative SPA1 (Sß=0.483, P<0.01) and preoperative IR (Sß=0.471, P<0.01), respectively. The CCT was the second factor that influenced postoperative DA ratio and SPA1 (Sß=-0.238, P<0.01; Sß=0.326, P<0.01). Conclusions: The changes of DA ratio, SPA1 and IR following SMILE increased with the decrease of RST or increase of PTA. With the RST<280 µm or the PTA>28%, the alteration of DA ratio significantly accelerated. Preoperative corneal biomechanical properties and postoperative CCT were main factors influencing corneal biomechanical properties after SMILE. (Chin J Ophthalmol, 2021, 57: 104-112).

[Assistant diagnose for subclinical keratoconus by artificial intelligence].

Objective: To investigate the diagnosis of normal cornea, subclinical keratoconus and keratoconus by artifical intelligence. Methods: Diagnostic study. From January 2016 to January 2019, who admitted to Tianjin Eye Hospital from 18 to 48 years old, with an average of (28.4±8.2) years of myopia patients in 2 018 cases. Two experienced ophthalmologists labeled keratoconus, subclinical keratconus and nomal cornea based on the topography. The data of 80% (1 615 cases) patients were randomly selected as the training set by computer random sampling method, and the data of 20% (403 cases) patients were used as the verification set. Using the Gradient Boosting Decision Tree (GBDT) algorithm to extract 28 corneal parameters, and establish an algorithm model to diagnose the corneal condition of the patient, verify the diagnostic accuracy of the model by using the 10-fold cross-validation method, and evaluate the model using the receiver operating characteristic curve. Sensitivity and specificity with the original labeling and ophthalmic resident labeling. Results: The diagnostic accuracy of the model was 95.53%. The area under the receiver operating characteristic curve (AUC) of the validation set was 0.996 6. The accuracy of the model for diagnosis of subclinical keratoconus and normal cornea was 96.67%, the AUC of the validation set was 0.993 6; the accuracy of diagnosis of keratoconus and normal cornea was 98.91%, and the AUC of the validation set was 0.998 2. The diagnostic accuracy of the model is 95.53%, which is significantly better than the resident's 93.55%. Conclusion: The model established by artifical intelligence can diagnose the subclinical keratoconus with high accuracy, which can greatly improve the clinical diagnosis efficiency and accuracy of young and primary ophthalmologists. (Chin J Ophthalmol, 2019, 55: 911-915).

Diffusion kurtosis imaging in identifying the malignancy of lymph nodes during the primary staging of rectal cancer.

AIM: The aim was to assess the diagnostic value of diffusion kurtosis imaging (DKI) for discriminating between benign and malignant lymph nodes in patients with rectal carcinoma. METHOD: ighty-five patients with rectal adenocarcinoma who underwent total mesorectal excision of the rectum were studied. A total of 273 lymph nodes were harvested and subjected to histological analysis. Quantitative parameters [apparent diffusion parameter Dapp of the Gaussian distribution, apparent kurtosis coefficient Kapp and apparent diffusion coefficient (ADC)] of lymph nodes were derived from DKI. Differences and the diagnostic performance of these parameters were calculated by using the independent-samples t test and receiver operating characteristic curve analyses. RESULTS: The median Dapp and ADC values of metastatic lymph nodes were significantly greater than those of benign lymph nodes, whereas the median Kapp of metastatic lymph nodes was statistically less than that of normal lymph nodes. Dapp had the relatively highest area under the curve of 0.774. When 1126.15 × 10-6  mm2 /s was used as a Dapp threshold value, the sensitivity and specificity were 96.97% and 41.82%, respectively. CONCLUSION: DKI can help differentiate metastatic vs benign lymph nodes during the primary staging of rectal cancer.