Technology in Cancer Research & Treatment最新文献

This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8. In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.

Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.

Objective: Ubiquitin-specific peptidase 39 (USP39) plays a carcinogenic role in many cancers, but little research has been conducted examining whether it is involved in head and neck squamous cell carcinoma (HNSCC). Therefore, this study explored the functional role of USP39 in HNSCC. Method: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed proteins (DEPs) between the HNSCC tumor and adjacent healthy tissues. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the functional enrichment of DEPs. Immunohistochemistry was used to detect protein expression. The viability and migration of two HNSCC cell lines, namely CAL27 and SCC25, were detected using the cell counting kit-8 assay and a wound healing assay, respectively. Quantitative real-time PCR was used to detect the expression level of signal transducer and activator of transcription 1 (STAT1) mRNA. Results: LC-MS/MS results identified 590 DEPs between HNSCC and adjacent tissues collected from 4 patients. Through GO and KEGG pathway analyses, 34 different proteins were found to be enriched in the spliceosome pathway. The expression levels of USP39 and STAT1 were significantly higher in HNSCC tumor tissue than in adjacent healthy tissue as assessed by LC-MS/MS analysis, and the increased expression of USP39 and STAT1 protein was confirmed by immunohistochemistry in clinical samples collected from 7 additional patients with HNSCC. Knockdown of USP39 or STAT1 inhibited the viability and migration of CAL27 and SCC25 cells. In addition, USP39 knockdown inhibited the expression of STAT1 mRNA in these cells. Conclusion: Our findings indicated that USP39 knockdown may inhibit HNSCC viability and migration by suppressing STAT1 expression. The results of this study suggest that USP39 may be a potential new target for HNSCC clinical therapy or a new biomarker for HNSCC.

Objectives: Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.

Methods: The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.

Results: Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.

Conclusion: This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.

Male breast cancer (MBC), one of the rare types of cancer among men where the global incidence rate is 1.8% of all breast cancers cases with a yearly increase in a pace of 1.1%. Since the last 10 years, the incidence has been increased from 7.2% to 10.3% and the mortality rate was decreased from 11% to 3.8%. Nevertheless, the rate of diagnoses has been expected to be around 2.6% in the near future, still there is a great lack in studies to characterize the MBC including the developed countries. Based on our search, it is evidenced from the literature that the number of risk factors for the cause of MBC are significant, which includes the increase in age, family genetic history, mutations in specific genes due to various environmental impacts, hormonal imbalance and unregulated expression receptors for specific hormones of high levels of estrogen or androgen receptors compared to females. MBCs are broadly classified into ductal and lobular carcinomas with further sub-types, with some of the symptoms including a lump or swelling in the breast, redness of flaky skin in the breast, irritation and nipple discharge that is similar to the female breast cancer (FBC). The most common diagnostic tools currently in use are the ultrasound guided sonography, mammography, and biopsies. Treatment modalities for MBC include surgery, radiotherapy, chemotherapy, hormonal therapy, and targeted therapies. However, the guidelines followed for the diagnosis and treatment modalities of MBC are mostly based on FBC that is due to the lack of prospective studies related to MBC. However, there are distinct clinical and molecular features of MBC, it is a need to develop different clinical methods with more multinational approaches to help oncologist to improve care for MBC patients.

Background: Strategies to minimize the impact of the COVID-19 pandemic led to a reduction in diagnostic testing. It is important to assess the magnitude and duration of this impact to plan ongoing care and avoid long-lasting impacts of the pandemic. Objective: We examined the association between the COVID-19 pandemic and the rate of diagnostic tests for breast, cervical, and colorectal cancer in Manitoba, Canada. Design and Participants: A population-based, cross-sectional study design with an interrupted time series analysis was used that included diagnostic tests from January 1, 2015 until August 31, 2022. Setting: Manitoba, Canada. Main Outcomes: Outcomes included mammogram, breast ultrasound, colposcopy, and colonoscopy rates per 100,000. Cumulative and percent cumulative differences between the fitted and counterfactual number of tests were estimated. Mean, median, and 90th percentile number of days from referral to colonoscopy date by referral type (elective, semiurgent, urgent) were determined. Results: In April 2020, following the declaration of the COVID-19 public health emergency, bilateral mammograms decreased by 77%, unilateral mammograms by 70%, breast ultrasounds by 53%, colposcopies by 63%, and colonoscopies by 75%. In Winnipeg (the largest urban center in the province), elective and semiurgent colonoscopies decreased by 76% and 39%, respectively. There was no decrease in urgent colonoscopies. As of August 2022, there were an estimated 7270 (10.7%) fewer bilateral mammograms, 2722 (14.8%) fewer breast ultrasounds, 836 (3.3%) fewer colposcopies, and 11 600 (13.8%) fewer colonoscopies than expected in the absence of COVID-19. As of December 2022, in Winnipeg, there were an estimated 6030 (23.9%) fewer elective colonoscopies, 313 (2.6%) fewer semiurgent colonoscopies, and 438 (27.3%) more urgent colonoscopies. Conclusions: In Manitoba, the COVID-19 pandemic was associated with sizable decreases in diagnostic tests for breast, colorectal, and cervical cancer. Two and a half years later, there remained large cumulative deficits in bilateral mammograms, breast ultrasounds, and colonoscopies.

Purpose: A daily quality assurance (QA) check in proton therapy is ensuring that the range of each proton beam energy in water is accurate to 1 mm. This is important for ensuring that the tumor is adequately irradiated while minimizing damage to surrounding healthy tissue. It is also important to verify the total charge collected against the beam model. This work proposes a time-efficient method for verifying the range and total charge of proton beams at different energies using a multilayer Faraday collector (MLFC).

Methods: We used an MLFC-128-250 MeV comprising 128 layers of thin copper foils separated by thin insulating Kapton^TM layers. Protons passing through the collector induce a charge on the metallic foils, which is integrated and measured by a multichannel electrometer. The charge deposition on the foils provides information about the beam range.

Results: Our results show that the proton beam range obtained using MLFC correlates closely with the range obtained from commissioning water tank measurements for all proton energies. Upon applying a range calibration factor, the maximum deviation is 0.4 g/cm². The MLFC range showed no dependence on the number of monitor units and the source-to-surface distance. Range measurements collected over multiple weeks exhibited stability. The total charge collected agrees closely with the theoretical charge from the treatment planning system beam model for low- and mid-range energies.

Conclusions: We have calibrated and commissioned the use of the MLFC to easily verify range and total charge of proton beams. This tool will improve the workflow efficiency of the proton QA.