Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer.

BACKGROUND: Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task. AIM: To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC. METHODS: This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm. RESULTS: Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%). CONCLUSION: Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.

Radiomics for predicting perineural invasion status in rectal cancer.

BACKGROUND: Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging. AIM: To establish a radiomics model for evaluating PNI status preoperatively in RC patients. METHODS: This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS: One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort). CONCLUSION: The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

Shaft Function of Kinesin-1's α4 Helix in the Processive Movement.

INTRODUCTION: Kinesin-1 motor is a molecular walking machine constructed with amino acids. The understanding of how those structural elements play their mechanical roles is the key to the understanding of kinesin-1 mechanism. METHODS: Using molecular dynamics simulations, we investigate the role of a helix structure, α4 (also called switch-II helix), of kinesin-1's motor domain in its processive movement along microtubule. RESULTS: Through the analysis of the structure and the interactions between α4 and the surrounding residues in different nucleotide-binding states, we find that, mechanically, this helix functions as a shaft for kinesin-1's motor-domain rotation and, structurally, it is an amphipathic helix ensuring its shaft functioning. The hydrophobic side of α4 consists strictly of hydrophobic residues, making it behave like a lubricated surface in contact with the core ß-sheet of kinesin-1's motor domain. The opposite hydrophilic side of α4 leans firmly against microtubule with charged residues locating at both ends to facilitate its positioning onto the intra-tubulin groove. CONCLUSIONS: The special structural feature of α4 makes for an effective reduction of the conformational work in kinesin-1's force generation process.

[Effects of central corneal thickness and corneal curvature on measurement of intraocular pressure with Goldmann applanation tonometer and non-contact tonometer].

OBJECTIVE: To investigate the effects of central corneal thickness (CCT) and corneal curvature (CC) on intraocular pressure (IOP) measurements by the Goldmann applanation tonometer (GAT) and the non-contact tonometer (NCT). METHODS: One hundred and twenty patients were recruited from the clinic of Peking Union Medical College Hospital. The CCT was measured by ultrasound pachymetry and the mean radius of CC by using Canon PK-5 refractometer. The IOP of each eye was measured by both GAT and NCT. Linear regression was used to compare the measurements of GAT and NCT; multi regression was used to analyze the relationships between CCT, CC and the measurements of GAT and NCT. Bland-Altman method was used to compare the effect of NCT and GAT on the IOP measurements. RESULTS: The results of the right eyes were reported in this paper. The mean and standard deviation of IOP measured by GAT and NCT was (18.4 + or - 4.0) mm Hg (1 mm Hg = 0.133 kPa) and (17.0 + or - 4.6) mm Hg, respectively, the difference was statistically significant (r = 0.835, P = 0.000). IOP measured using GAT increased by 0.039 mm Hg per microm increase in CCT. IOP measured using NCT increased by 0.064 mm Hg per microm increase in CCT. For an increase of 1 mm of mean corneal curvature there was decrease in IOP of 2.648 mm Hg measured by the GAT and of 3.190 mm Hg measured by the NCT. Compared to the GAT, NCT underestimated at low IOP level and overestimated at higher IOP level. CONCLUSIONS: The IOP measurement obtained with both GAT and NCT varied with CCT and CC. CCT affected IOP measurements by NCT more than that by GAT.

[Analysis of the effectiveness of laser in situ keratomileusis two years after surgery].

OBJECTIVE: To evaluate the effectiveness of laser in situ keratomileusis (LASIK) for myopia after 2 year follow-up. METHODS: LASIK surgery was performed for 95 eyes (49 cases) with refractive dioptres (from -1.00 to -16.50 D), and then followed up for more than 2 years. The preoperative spherical equivalent was -1.00(-)-16.50 (-6.73 +/- 2.02) D. Patients were divided into two groups including group I with mild to moderate myopia (< or = -6.00 D) and Group II with severe myopia (more than -6.00 D). RESULTS: In group I, uncorrected visual acuity more than and equivalent to 1.0 was in 40 eyes (93.02%), more than and equivalent to 0.8 in 43 eyes (100%). In group II, uncorrected visual acuity larger than and same as 1.0 was in 37 eyes (71.15%), larger than and same as 0.8 in 45 eyes (86.54%), larger than and same as 0.5 in 50 eyes (96.15%), and less than 0.5 in 2 eyes (3.85%). The mean spherical equivalent changed from (-4.14 +/- 0.86) D to (-0.04 +/- 0.14) D in group I and from (-9.00 +/- 1.64) D to (-0.08 +/- 0.20) D in group II. Significant difference was seen in these two groups (P < 0.001). The keratometric powers (K) were reduced from (44.31 +/- 0.78) to (40.11 +/- 1.23) and from (45.46 +/- 1.00) to (38.01 +/- 1.73) respectively, which showed significant difference (P < 0.001). CONCLUSION: The effectiveness of LASIK for mild to moderate myopia or severe myopia is promising and safe.

[Static and dynamic contrast sensitivity of myopic eyes before and after laser in situ keratomileusis].

OBJECTIVE: To evaluate the static and dynamic contrast sensitivity changes in myopic patients before and after laser in situ keratomileusis (LASIK). METHODS: Seventy-three eyes in 37 patients with myopia (with or without astigmatism) who received LASIK were tested for static and dynamic contrast sensitivities using the METRO VISION MON ELEC I system at 0.7, 1.4, 2.7, 5.5, 11, and 22 cpd and cps prior to LASIK, and at one-, three-, and six-month intervals after LASIK. RESULTS: All eyes gained naked visual acuity of more than 0.5 after LASIK. The contrast sensitivity was depressed at all frequencies 1 month after LASIK, as compared to one week prior to LASIK. The depression at 2.7, 5.5, 11 (P < 0.01) and 22 cpd (P < 0.05) was statistically significant for static contrast sensitivity, and also at 5.5 (P < 0.01) and 11 cps (P < 0.05) for dynamic contrast sensitivity. Myopic eyes between 6.25 D and 14.0 D, and astigmatic eyes 2 DC and more, suffered more static and dynamic contrast sensitivity depression than the myopic eyes between 1.25 D and 6.00 D and astigmatic eyes less than 2 DC. Contrast sensitivities were improved and exceeded preoperative levels 3 months after LASIK, and improved even more 6 months after LASIK. All sequences were statistically significant for static contrast sensitivity (P < 0.01), while only 2.7, 5.5, and 11 cps were statistically significant for dynamic contrast sensitivity (P < 0.01). The astigmatic eyes 2 DC and more showed less improvement, even below the preoperative level at 1.4 cps of dynamic contrast sensitivity. CONCLUSIONS: While temporary depression of contrast sensitivity for myopic eyes after LASIK was seen, contrast sensitivity soon returned to exceed preoperative levels at 3 months after LASIK, while improving even more 6 months after LASIK.