Safety and efficacy of indocyanine green fluorescence imaging for real-time guidance of laparoscopic thermal ablation in patients with liver cancer.

PURPOSE: To evaluate the safety and efficacy of indocyanine green fluorescence imaging for real-time guidance of laparoscopic thermal ablation in patients with liver cancer. MATERIALS AND METHODS: A total of 27 patients with 40 liver lesions underwent fluorescence-assisted laparoscopic ablation between January 2020 to March 2023. The sensitivity of indocyanine green (ICG)-fluorescence imaging, technique effectiveness rate and complications of fluorescence-assisted laparoscopic thermal ablation were evaluated. RESULTS: In total, 33 out of the 40 lesions were identified by ICG-fluorescence imaging technique, with the sensitivity of 82.5%. The sensitivity of ICG-fluorescence imaging of tumor detection in liver surface of parenchyma was significantly higher than that in the deeply located hepatic parenchyma (96.8% vs 33.3%, p = 0.002). ICG-fluorescence imaging procedures detected 4 lesions that cannot be seen on intraoperative ultrasound. It provides clear demarcation lines on the hepatic surface. Technical success is achieved if the necrotic zone had at least a 5 mm ablative margin around the outer edge of the ICG-fluorescence image. Technical success of fluorescence laparoscopic radiofrequency ablation (FLRFA) and fluorescence laparoscopic microwave ablation (FLMWA) was 100% (27/27). Technical effectiveness is defined by the complete necrotic lesions of the local tumor tissue during follow-up. According to the CT/MRI one month after FLRFA or FLMWA, the technical efficacy rate was 92.5% (37/40) and local tumor progression occurred in 7.5% (3/40) of the enrolled lesions. During the follow-up period, no major complications were observed. CONCLUSION: ICG-fluorescence imaging guided laparoscopic thermal ablation was feasible, safe and effective.

Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality.

Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.

Pathologic complete response of hepatoid adenocarcinoma of the stomach after chemo-immunotherapy: A rare case report and literature review.

Background: Hepatoid adenocarcinoma of the stomach (HAS) is a highly malignant subtype of gastric carcinoma with specific clinicopathological features and extremely poor prognosis. We present an exceedingly rare case of complete response after chemo-immunotherapy. Case Description: A 48-year-old woman with highly elevated serum alpha-fetoprotein (AFP) level was found to have HAS verified by pathological examination based on gastroscopy. Computed tomography scan was done and TNM staging of the tumor was T4aN3aMx. Programmed cell death ligand-1 (PD-L1) immunohistochemistry was performed, revealing a negative PD-L1 expression. Chemo-immunotherapy including oxaliplatin plus S-1 and PD-1 inhibitor terelizumab was given to this patient for 2 months until the serum AFP level decreased from 748.5 to 12.9 ng/mL and the tumor shrank. D2 radical gastrectomy was then performed and histopathology of the resected specimen revealed that the cancerous cells had disappeared. Pathologic complete response (pCR) was achieved and no evidence of recurrence has been found after 1 year of follow-up. Conclusions: We, for the first time, reported an HAS patient with negative PD-L1 expression who achieved pCR from the combined chemotherapy and immunotherapy. Although no consensus has been reached regarding the therapy, it might provide a potential effective management strategy for HAS patient.

Removal of Dust Microelectric Signal Based on Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis.

Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.

Tumor-Infiltrating Immune Cells Act as a Marker for Prognosis in Colorectal Cancer.

Tumor-infiltrating immune cells (TIICs) play essential roles in cancer development and progression. However, the association of TIICs with prognosis in colorectal cancer (CRC) patients remains elusive. Infiltration of TIICs was assessed using ssGSEA and CIBERSORT tools. The association of TIICs with prognosis was analyzed in 1,802 CRC data downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/) databases. Three populations of TIICs, including CD66b+ tumor-associated neutrophils (TANs), FoxP3+ Tregs, and CD163+ tumor-associated macrophages (TAMs) were selected for immunohistochemistry (IHC) validation analysis in 1,008 CRC biopsies, and their influence on clinical features and prognosis of CRC patients was analyzed. Prognostic models were constructed based on the training cohort (359 patients). The models were further tested and verified in testing (249 patients) and validation cohorts (400 patients). Based on ssGSEA and CIBERSORT analysis, the correlation between TIICs and CRC prognosis was inconsistent in different datasets. Moreover, the results with disease-free survival (DFS) and overall survival (OS) data in the same dataset also differed. The high abundance of TIICs found by ssGSEA or CIBERSORT tools can be used for prognostic evaluation effectively. IHC results showed that TANs, Tregs, TAMs were significantly correlated with prognosis in CRC patients and were independent prognostic factors (PDFS ≤ 0.001; POS ≤ 0.023). The prognostic predictive models were constructed based on the numbers of TANs, Tregs, TAMs (C-indexDFS&OS = 0.86; AICDFS = 448.43; AICOS = 184.30) and they were more reliable than traditional indicators for evaluating prognosis in CRC patients. Besides, TIICs may affect the response to chemotherapy. In conclusion, TIICs were correlated with clinical features and prognosis in patients with CRC and thus can be used as markers.