Digital image correlation-assisted phase-sensitive optical coherence tomography.

In this Letter, we introduce a digital image correlation-assisted (DIC-assisted) method to tackle the challenges of phase decorrelation and the inability to measure lateral displacement in phase-sensitive optical coherence tomography (PhS-OCT). This DIC-assisted PhS-OCT (DIC-PhS-OCT) first employs DIC to track displacements from the measured amplitude spectra and subsequently uses these tracked displacements to correct supra-pixel displacement offsets in the measured phase spectra. As a result, it effectively mitigates phase decorrelation resulting from both axial and lateral displacements while enabling the acquisition of sub-pixel-level lateral displacements during the DIC computation. Our experiments demonstrate the effectiveness of DIC-PhS-OCT in addressing these challenges while retaining the ultrahigh sensitivity of conventional PhS-OCT.

A new nomogram for assessing complete response (CR) in gastric diffuse large B-cell lymphoma (DLBCL) patients after chemotherapy.

PURPOSE: Achieving complete response (CR) after first-line chemotherapy in gastric DLBCL patients often results in longer disease-free survival. We explored whether a model based on imaging features combined with clinicopathological factors could assess the CR to chemotherapy in patients with gastric DLBCL. METHODS: Univariate (P < 0.10) and multivariate (P < 0.05) analyses were used to identify factors associated with a CR to treatment. As a result, a system was developed to evaluate whether gastric DLBCL patients had a CR to chemotherapy. Evidence was found to support the model's ability to predict outcomes and demonstrate clinical value. RESULTS: We retrospectively analysed 108 people who had been diagnosed gastric DLBCL; 53 were in CR. Patients were divided at random into a 5:4 training/testing dataset split. ß2 microglobulin before and after chemotherapy and lesion length after chemotherapy were independent predictors of the CR of gastric DLBCL patients after chemotherapy. These factors were used in the predictive model construction. In the training dataset, the area under the curve (AUC) of the model was 0.929, the specificity was 0.806, and the sensitivity was 0.862. In the testing dataset, the model had an AUC of 0.957, specificity of 0.792, and sensitivity of 0.958. The AUC did not differ significantly between the training and testing dates (P > 0.05). CONCLUSION: A model constructed using imaging features combined with clinicopathological factors could effectively evaluate the CR to chemotherapy in gastric DLBCL patients. The predictive model can facilitate the monitoring of patients and be used to adjust individualised treatment plans.

Clinical value of combined preoperative-postoperative neutrophil-to-lymphocyte ratio in predicting hepatocellular carcinoma prognosis after radiofrequency ablation.

OBJECTIVE: Previous studies focused on the prognostic significance of the pre- or post-operative neutrophil-lymphocyte ratio (NLR); the significance of combined pre- and post-operative NLR (PP-NLR) remains unknown. Therefore, we investigated the value of PP-NLR for predicting prognosis after radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC) to improve treatment and prolong survival. METHODS: We investigated pre- and post-operative NLR and PP-NLR in predicting prognosis after RFA in patients with HCC. Optimal thresholds for leukocytes, lymphocytes, neutrophils, and NLR before and after RFA were retrospectively assessed in patients with HCC who had undergone RFA between January 2018 and June 2019 in Harbin Medical University Cancer Hospital. Risk factors for early HCC recurrence and those affecting recurrence-free survival (RFS) were analyzed. RESULTS: The respective pre- and post-operative optimal thresholds were as follows: neutrophils, 3.431 and 4.975; leukocytes, 5.575 and 6.61; lymphocytes, 1.455 and 1.025; and NLR, 1.53 and 4.36. Univariate analysis revealed tumor number; alpha-fetoprotein level; post-operative leukocytes, lymphocytes, NLR, and neutrophils; pre-operative neutrophils and NLR; and PP-NLR as factors influencing early recurrence and RFS. Multivariate analysis indicated PP-NLR as an independent risk factor for poor RFS and early recurrence. CONCLUSION: PP-NLR was more effective for predicting prognosis than pre- or post-operative NLR alone for patients with HCC. ADVANCES IN KNOWLEDGE: The novelty of this study lies in the combination of pre- and post-operative NLR, namely PP-NLR, to study its prognostic value for HCC patients after RFA, which has not been found in previous studies. The contribution of our study is that PP-NLR can provide clinicians with a new reference index to judge the prognosis of patients and make timely treatment to help patients improve their prognosis.

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis.

The purpose of this study was to screen for high-risk factors leading to the early recurrence of hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) and to construct a prediction model and nomogram. This retrospective study included 108 patients with primary HCC who underwent RFA treatment at the Harbin Medical University Cancer Hospital between January 2018 and June 2019. Four risk factors were screened for using univariate and multivariate logistic regression analyses: number of tumors (hazard ratio [HR] = 14.684, 95% confidence interval [CI]: 1.099-196.215, p = 0.042), neutrophil-to-lymphocyte ratio (NLR) (HR = 2.178, 95% CI: 1.003-4.730, p = 0.049), contrast-enhanced ultrasound (CEUS) performance (HR = 6.482, 95% CI: 1.161-36.184, p = 0.033) and α-fetoprotein (AFP) level (HR = 1.001, 95% CI: 1.000-1.003, p = 0.040). We established a prediction model: Logit(p) = -3.096 + 2.827 × (number of tumors >1 = 1) + 1.851 × (CEUS revealing rapid enhancement of blood flow signal in the arterial phase and clearance in the portal phase = 1) + 1.941 × (NLR >1.55 = 1) + 0.257 × (AFP >32.545 = 1). Through clinical decision curve analysis, the model's threshold was 0.043-0.873, indicating a high clinical value. Patients with a high AFP level, typical CEUS enhancement pattern, multiple tumors and elevated NLR are more likely to relapse early.