Diagnostic efficacy of SEPT9 and PAX5 gene methylation in gastrointestinal cancer and precancerous lesions.

To assess the diagnostic efficacy of SEPT9 along with PAX5 gene methylation detection in gastrointestinal cancer and precancerous lesions, the peripheral blood of 62 patients with gastric cancer (GC) and 60 patients with no evidence of disease (as the control group) were retrospectively collected. The methylation rates of PAX5 and SEPT9 gene promoters in blood samples of GC group were detected by PCR. At the same time, the differences in methylation rates of genes in the two groups were compared, and the predictive value of plasma methylation PAX5 and SEPT9 in GC was evaluated by receiver operating characteristic (ROC) curve. We found that there were 41 cases of methylated PAX5 gene promoter region and 39 cases of methylated SEPT9 gene promoter region in GC group. The control group contained 14 cases of PAX5 gene promoter methylation and 12 cases of RNF¹80 gene promoter methylation. The occurrence of PAX5 promoter methylation was correlated with age of GC patients. There were statistically significant differences in mSEPT9 gene in patients with different TNM stages. Kaplan-Meier survival curve analysis revealed that the three-year overall survival rate of GC patients with PAX5 methylation was lower than that of GC patients without PAX5 methylation. No significant difference was discovered in 3-year overall survival rate between GC patients with SEPT9 methylation and those without SEPT9 methylation. Combined detection could not improve the diagnostic value of GC, but could promote diagnosis sensitivity. In summary, the risk of PAX5 and SEPT9 gene methylation in GC patients presents higher when compared with healthy people. PAX5 gene methylation is closely related to age, while SEPT9 is closely related to tumor TNM stage, and PAX5 gene methylation can decrease the survival rate of GC patients. Detection of PAX5 gene methylation level can assist in evaluating the prognosis of GC patients.

Cervical cancer screening and treatment costing in Senegal.

Introduction: in Senegal, cervical cancer is the leading cause of cancers among women. This study estimated the costs associated with cervical cancer screening and treatment for precancerous lesions from the health system perspective. Methods: we estimated costs for screening, diagnostics, and treatment. We conducted a cross-sectional study in seven regions with primary data collected from 50 health facilities. Data collection included structured questionnaires, with secondary data from the Ministry of Health and other sources. A mixed-methods approach combined ingredients-based costing and financial expenditures to estimate direct medical and non-medical costs. All costs are reported in 2019 USD. Results: average costs were $3.71 for visual inspection with acetic acid, $16.49 for Pap smear, and $46.65 for human papillomavirus deoxyribonucleic acid (HPV DNA) testing. Screening cost drivers were clinical exam supplies and clinical equipment for visual inspection with acetic acid, offsite processing of specimens for Pap smear, and lab equipment costs for HPV DNA procedure. The average cost of diagnosis via colposcopy alone was $25.73, and colposcopy with biopsy/endocervical curettage was $74.96. The average cost of treatment followed by one visit for pre-cancerous lesions was $195.24 for loop electrosurgical excision, $47.35 for cryotherapy, and $32.35 for thermal ablation. Clinical equipment and lab costs were the largest contributors to colposcopy and endocervical curettage/biopsy expenses. Clinical equipment made up the largest portion of cryotherapy, loop electrosurgical excision, and thermoablation costs. Conclusion: this study is the first to estimate the costs of HPV screening and treatment in Senegal, which can be used to inform decision-making on cervical cancer investments.

Serum and salivary interleukin-1ß level in oral precancer: An observational study.

BACKGROUND AND AIM: Precancer biomarkers help in early detection and management of oral potentially malignant disorders (OPMDs). Interleukin-1ß (IL-1ß), a biomarker, is known to be altered in oral submucous fibrosis (OSMF) and oral leukoplakia (OL). Therefore, we evaluated and compared the serum and salivary IL-1ß levels in patients with OSMF/oral leukoplakia and in gender- and age-matched healthy individuals. MATERIALS AND METHODS: An in vivo, prospective, observational study was conducted on 40 subjects. Subjects were divided into two groups with 20 individuals in each group, that is, Group I: OSMF/oral leukoplakia and Group II: control group. Salivary and serum IL-1ß levels were quantitatively estimated using enzyme-linked immunosorbent assay (ELISA). The statistical tests used were unpaired t-test and Chi-square test. RESULTS: The serum IL-1ß levels were significantly (P 0.001) lesser in Group I in comparison to Group II. The salivary IL-1ß levels remained insignificant between both the groups. However, in both the groups, the salivary IL-1ß levels were significantly higher compared to the serum IL-1ß levels. CONCLUSION: We found that the serum IL-1ß level can be considered as a prospective biomarker for dysplasia, whereas salivary IL-1ß alone needs more elaborated studies to account for its application as a potential biomarker in OPMD.

A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders.

OBJECTIVES: This study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia. MATERIALS AND METHODS: Following ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team. RESULTS: A dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits. CONCLUSION: Researchers can utilize the annotated images in the COCO format, along with the patients' metadata, to enhance ML and AI algorithm development.

Prediction of precancerous cervical cancer lesions among women living with HIV on antiretroviral therapy in Uganda: a comparison of supervised machine learning algorithms.

BACKGROUND: Cervical cancer (CC) is among the most prevalent cancer types among women with the highest prevalence in low- and middle-income countries (LMICs). It is a curable disease if detected early. Machine learning (ML) techniques can aid in early detection and prediction thus reducing screening and treatment costs. This study focused on women living with HIV (WLHIV) in Uganda. Its aim was to identify the best predictors of CC and the supervised ML model that best predicts CC among WLHIV. METHODS: Secondary data that included 3025 women from three health facilities in central Uganda was used. A multivariate binary logistic regression and recursive feature elimination with random forest (RFERF) were used to identify the best predictors. Five models; logistic regression (LR), random forest (RF), K-Nearest neighbor (KNN), support vector machine (SVM), and multi-layer perceptron (MLP) were applied to identify the out-performer. The confusion matrix and the area under the receiver operating characteristic curve (AUC/ROC) were used to evaluate the models. RESULTS: The results revealed that duration on antiretroviral therapy (ART), WHO clinical stage, TPT status, Viral load status, and family planning were commonly selected by the two techniques and thus highly significant in CC prediction. The RF from the RFERF-selected features outperformed other models with the highest scores of 90% accuracy and 0.901 AUC. CONCLUSION: Early identification of CC and knowledge of the risk factors could help control the disease. The RF outperformed other models applied regardless of the selection technique used. Future research can be expanded to include ART-naïve women in predicting CC.

Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions.

PURPOSE: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. MATERIALS AND METHODS: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). RESULTS: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. CONCLUSIONS: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

Exploring patient experiences of surveillance for pancreatic cystic neoplasms: a qualitative study.

BACKGROUND: Pancreatic cystic neoplasms (PCN) are considered premalignant conditions to pancreatic adenocarcinoma with varying degrees of cancerous potential. Management for individuals who do not require surgical treatment involves surveillance to assess for cancerous progression. Little is known about patients' experience and the impact of living with surveillance for these lesions. AIMS: To explore the experiences of patients living with surveillance for PCNs. METHODS: Semi-structured qualitative interviews were conducted with patients under surveillance for pancreatic cystic neoplasms in the UK. Age, gender, time from surveillance and surveillance method were used to purposively sample the patient group. Data were analysed using reflexive thematic analysis. RESULTS: A PCN diagnosis is incidental and unexpected and for some, the beginning of a disruptive experience. How patients make sense of their PCN diagnosis is influenced by their existing understanding of pancreatic cancer, explanations from clinicians and the presence of coexisting health concerns. A lack of understanding of the diagnosis and its meaning for their future led to an overarching theme of uncertainty for the PCN population. Surveillance for PCN could be seen as a reminder of fears of PCN and cancer, or as an opportunity for reassurance. CONCLUSIONS: Currently, individuals living with surveillance for PCNs experience uncertainty with a lack of support in making sense of a prognostically uncertain diagnosis with no immediate treatment. More research is needed to identify the needs of this population to make improvements to patient care and reduce negative experiences.

IMPROVEMENT OF THE MODEL OF PREVENTION OF MALIGNANT NEOPLASM OF THE GASTRIC.

The fight against malignant neoplasms is one of the most important problems of health care in Ukraine; its relevance is due to the continuous growth of oncological morbidity in the population, the complexity of timely diagnosis and treatment, high cost, as well as quite high levels of disability and mortality of such patients. Gastric cancer, which remains one of the most common and deadly neoplasms in the world, occupies one of the leading positions among cancer. Aim - scientifically substantiate and develop a model for improving the organization of prevention of malignant neoplasms of the gastric. A study of performance indicators of oncology health care facilities and a survey of respondents was conducted: 180 respondents of patients with gastric cancer and precancerous diseases of the stomach using medical-statistical, sociological methods and questionnaires. A functional and organizational model for improving the prevention of malignant neoplasms of the stomach has been scientifically substantiated and developed. The features of the proposed model were the inclusion in it, in addition to the previously existing, innovative elements (an algorithm for early diagnosis and prevention of negative consequences of malignant neoplasms of the stomach at the level of primary medical care, reminders for primary medical care doctors regarding monitoring of risk factors and predictors of malignancy of precancerous stomach diseases, the allocation of a dynamic monitoring group due to the increased risk of precancerous gastric diseases becoming oncological), as well as previously existing, but functionally changed components (optimization of the functions of the primary care physician in relation to the information provision of the patient and his relatives; monitoring of risk factors for precancerous and cancerous stomach diseases, control and accounting for the implementation of the recommendations of specialist doctors and rehabilitation specialists), the interaction between which provided the model with a qualitatively new focus on achieving its strategic goal - preventing the occurrence and progression of the development of malignant neoplasms of the gastric. The proposed functional and organizational model will lead to a positive medical and social effect for the improvement of the organization of the prevention of gastric cancer in the main areas: systematicity, comprehensiveness and preventive direction. Its implementation will lead to an increase in early detection, coverage of dynamic monitoring of patients, as well as a projected economic effect due to a decrease in the specific weight of neglected forms of gastric cancer, improvement in survival and reduction in mortality.

Detection of Cervical Precancer Using Visual Inspection Method with Acetic Acid.

BACKGROUND: Cervical cancer screening is the priority activity of the government. Visual inspection with Acetic acid (VIA), Pap smear Liquid-based cytology, and HPV DNA testing are different methods of screening. VIA-based screening is the cost-effective method of screening in a resource-constrained setting like in our country as this doesn't require cyto-histological testing, can be performed by trained paramedics too, and is as accurate as a cytological test. The aim is to explore pre-cancer cervical lesions by screening women in the community by visual inspection using acetic-acid. METHODS: Community-based cross-sectional study done at a health camp setting for three months from March to June 2023. The married non-pregnant women of 30-60 years were screened. Descriptive tests as well as sub-group analysis performed by Chi-Square tests. RESULTS: From ten community health camps, 1255 cases were screened and screen positivity was 14.3%. Positive results were proportionately distributed to all parity by 13-19%. Half of the positive results (47.2%) were in the 34-40 age group. There were no significant differences in screen positivity by parity or vaginal discharge. Menopausal women had 7.7% screen positivity. CONCLUSIONS: The prevalence of VIA positivity in the community was found double the previous facility-based prevalence; and there was no significant difference by parity, menopausal status, and vaginal discharge. The positivity was more in 30-45 years of age.

GC-MS-based untargeted plasma metabolomics identifies a 2-biomarker panel for possible diagnosis of precancerous cervical intraepithelial neoplasia stages from cervical cancer.

Cervical cancer (CC) remains a major health concern globally, much of the brunt of which is experienced by the low- and middle-income countries where screening in terms of cytology and DNA genotyping for the high-risk oncogenic subtypes of the human papilloma virus (hr-HPV) is either inadequate or performed rather late. In this study, we aimed to determine biomarkers or panels of biomarkers that are capable of diagnosing the precancerous cervical intraepithelial neoplasia (CIN) stages from healthy and CC patients via untargeted gas chromatography-mass spectrometry-based metabolomics. Various cross-comparisons were conducted from which differential metabolites were identified. The underlying metabolic pathways based on the differential metabolites identified from the various cross-comparisons mainly related to amino acids biosynthesis and metabolism and steroid hormone biosynthesis. From all cross-comparisons, two common metabolites namely, 2-methyl-1-propylamine (also known as isobutylamine) and estrone were found to possess excellent to good diagnostic abilities, especially in distinguishing the early stages of CIN (CIN I, CIN II) from healthy women and CC patients. These findings have clinical significance in the sense that, once validated the 2-biomarker panel could be adopted in clinical practice for early diagnosis of CIN and invasive carcinoma. This would therefore inform the choice of treatment to be initiated by the clinician.

[Artificial Intelligence: accuracy for the diagnosis of precancerous lesions of the cervix]. / Inteligencia Artificial: precisión diagnóstica de lesiones preneoplásicas de cérvix uterino.

INTRODUCTION: To compare the diagnostic sensitivity of artificial intelligence (AI) assisted videocolposcopy with standard videocolposcopy performed by specialist colposcopists. METHODS: A descriptive retrospective cross-sectional study, 782 anonymized medical records from the Computerized System for Screening (SITAM) of women who underwent videocolposcopy with AI and colposcopy with common videocolposcopy performed by specialists, with their corresponding biopsies (gold standard) were analyzed. The relationship between the results of IA videocolposcopy and regular videocolposcopy and the results of biopsies was evaluated. The overall accuracy of each diagnostic procedure was calculated. The sensitivity and concordance of the results of AI videocolposcopy with the gold standard (biopsy) were determined. RESULTS: A total of 395 patient records of patients with IA videocolposcopy and 387 with regular videocolposcopy were analyzed. The accuracy of results was 80% (IC 95%: 75-83%) in IA videocolposcopy and 65% (IC 95%: 60-69%) in regular videocolposcopy (p<0.001). Videocolposcopy results with IA and common colposcopy were significantly correlated with biopsy results, rs=0.75 vs. rs=0.57 respectively (p<0.001). The sensitivity of videocolposcopy with AI was 96% (95% CI: 94-98%), and 93% (95% CI: 89-95%) for regular colposcopy. The overall agreement of colposcopic impressions classified by videocolposcopy with AI and disease was higher than that of colposcopic interpretation by colposcopists (90% vs. 83%, Kappa 0.59 vs. 0.47, p<0.001). CONCLUSION: The high diagnostic accuracy of AI videocolposcopy allows obtaining highly sensitive studies that help in the early detection of precursor lesions of cervical neoplasia.

Introducción: Objetivo: comparar sensibilidad diagnóstica de videocolposcopia con inteligencia artificial (IA) auxiliar, con la videocolposcopia común realizada por colposcopistas. Métodos: Estudio descriptivo de corte transversal retrospectivo, en 782 historias clínicas anonimizadas del Sistema Informático para el Tamizaje (SITAM), de mujeres a las cuales se les efectuaron videocolposcopia con IA y colposcopías con videocolposcopio común realizadas por especialistas, con sus biopsias (gold standard). Se evaluó la relación entre los resultados de videocolposcopia con IA y videocolposcopia común con resultados de las biopsias. Se calculó precisión global de cada procedimiento diagnóstico. Se determinó sensibilidad y concordancia de los resultados de la videocolposcopia con IA, con el gold standard. Resultados: Se analizaron 395 historias clínicas de pacientes con videocolposcopia con IA y 387 con videocolposcopia común. La precisión diagnóstica de resultados fue 80% (IC 95%: 75-83%) en videocolposcopias con IA y 65% (IC 95%: 60-69%) en videocolposcopia común (p<0.001). Los resultados de videocolposcopia con IA y colposcopia común se correlacionaron significativamente con los resultados de las biopsias, rs=0.75 vs. r s=0.57 respectivamente (p<0.001). La sensibilidad de videocolposcopia con IA fue 96% (IC 95%: 94-98%), y 93% (IC 95%: 89-95%) en colposcopías comunes. La concordancia general de las impresiones colposcópicas clasificadas por videocolposcopia con IA y enfermedad fue mayor que la de la interpretación colposcópica de los colposcopistas (90% frente a 83%, Kappa 0.59 frente a 0.47, p<0.001). Conclusión: La alta precisión diagnóstica de videocolposcopia con IA permite aumentar la sensibilidad del estudio y mejorar la detección precoz de lesiones precursoras de neoplasias cervicouterinas.

[Development and validation of predictive models for esophageal squamous cell carcinoma and its precancerous lesions using terminal motif analysis in circulating cell-free DNA].

Objectives: To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions. Methods: Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set (n=284) and a validation set (n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1's efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training (n=243), validation (n=105), and test (n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model's accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results: We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1's sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer's progression (P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2's sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions: Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.

Diagnostic Usefulness of p53 Immunostaining in Gastric Cancer and Dysplasia: A Real-world Clinical Experience.

BACKGROUND/AIM: Gastric cancer and its precancerous lesions represent a significant public health concern. A subset of gastric cancers exhibits mutations in the TP53 gene, often accompanying distinctive morphologic alterations. This study aimed to assess the diagnostic efficacy of p53 immunostaining in real-world clinical settings. PATIENTS AND METHODS: A retrospective analysis was conducted on 50 cases of gastric tumors and tumor-like lesions, wherein p53 immunostaining played a pivotal diagnostic role. The staining pattern of p53 was examined in conjunction with clinicopathologic parameters. RESULTS: Mutant p53 staining pattern demonstrated a significant association with high-grade nuclear atypia (p<0.001), high-grade dysplasia, and tubular adenocarcinoma (p<0.001), as well as microsatellite instability status (p=0.034). Furthermore, the diagnostic utility of p53 immunostaining was evident in scenarios where: 1) biopsy specimens contained few tumor cells, 2) pathologic evaluation of resection margins was limited by cauterization artifacts, and 3) distinction between low-grade and high-grade gastric dysplasia was challenging. CONCLUSION: P53 immunostaining can be helpful for the diagnosis of gastric tumor and tumor-like lesions, and accurate pathologic margin evaluation, particularly in lesions demonstrating intestinal-type differentiation and some degree of nuclear atypia.

Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.

A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection.

OBJECTIVES: Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers. METHODS: 4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented. RESULTS: The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938. CONCLUSIONS: OSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective. CLINICAL RELEVANCE: Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.

A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions.

OBJECTIVE: A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation. Distinguishing OLCs from other oral potentially malignant disorders (OPMDs) can help prevent unnecessary concern or testing, but accurate identification by nonexpert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a 'cytomics-on-a-chip' tool and integrated predictive model for aiding the identification of OLCs. STUDY DESIGN: All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N = 94) and nonlichenoid (OLC-) (N = 237) histologic features in a population with OPMDs. RESULTS: The OLC Index discriminated OLC+ and OLC- subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (P = .0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology. CONCLUSIONS: The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis of patients presenting with OPMDs and OLCs.

Clinical significance of 18F-FDG-PET/CT for detection of incidental pre-malignant and malignant colonic lesions: correlation with colonoscopic and histopathological results.

BACKGROUND: Incidental colorectal fluorodeoxyglucose (FDG) uptake, observed during positron emission tomography/computed tomography (PET/CT) scans, attracts particular attention due to its potential to represent both benign and pre-malignant/malignant lesions. Early detection and excision of these lesions are crucial for preventing cancer development and reducing mortality. This research aims to evaluate the correlation between incidental colorectal FDG uptake on PET/CT with colonoscopic and histopathological results. METHODS: Retrospective analysis was performed on data from all patients who underwent PET/CT between December 2019 and December 2023 in our hospital. The study included 79 patients with incidental colonic FDG uptake who underwent endoscopy. Patient characteristics, imaging parameters, and the corresponding colonoscopy and histopathological results were studied. A comparative analysis was performed among the findings from each of these modalities. The optimal cut-off value of SUVmax for 18F-FDG PET/CT diagnosis of premalignant and malignant lesions was determined by receiver operating characteristic (ROC) curves. The area under the curve (AUC) of SUVmax and the combined parameters of SUVmax and colonic wall thickening (CWT) were analyzed. RESULTS: Among the 79 patients with incidental colorectal FDG uptake, histopathology revealed malignancy in 22 (27.9%) patients and premalignant polyps in 22 (27.9%) patients. Compared to patients with benign lesions, patients with premalignant and malignant lesions were more likely to undergo a PET/CT scan for primary evaluation (p = 0.013), and more likely to have focal GIT uptake (p = 0.001) and CWT (p = 0.001). A ROC curve analysis was made and assesed a cut-off value of 7.66 SUVmax (sensitivity: 64.9% and specificity: 82.4%) to distinguish premalignant and malignant lesions from benign lesions. The AUCs of the SUVmax and the combined parameters of SUVmax and CWT were 0.758 and 0.832 respectively. CONCLUSION: For patients undergo PET/CT for primary evaluation, imaging features of colorectal focal FDG uptake and CWT were more closely associated with premalignant and malignant lesions. The SUVmax helps determine benign and premalignant/malignant lesions of the colorectum. Moreover, the combination of SUVmax and CWT parameters have higher accuracy in estimating premalignant and malignant lesions than SUVmax.

Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

Analysis of the diagnostic performance of PAX1/SOX1 gene methylation in cervical precancerous lesions and its role in triage diagnosis.

Methylation panels, tools for investigating epigenetic changes associated with diseases like cancer, can identify DNA methylation patterns indicative of disease, providing diagnostic or prognostic insights. However, the application of methylation panels focusing on the sex-determining region Y-box 1 (SOX1) and paired box gene 1 (PAX1) genes for diagnosing cervical lesions is under-researched. This study aims to examine the diagnostic performance of PAX1/SOX1 gene methylation as a marker for cervical precancerous lesions and its potential application in triage diagnosis. From September 2022 to April 2023, 181 patients with abnormal HPV-DNA tests or cytological exam results requiring colposcopy were studied at Hubei Maternal and Child Health Hospital, China. Data were collected from colposcopy, cytology, HPV-DNA tests, and PAX1/SOX1 methylation detection. Patients were categorized as control, cervical intraepithelial neoplasia Grade 1 (CIN1), Grade 2 (CIN2), Grade 3 (CIN3), and cervical cancer (CC) groups based on histopathology. We performed HPV testing, liquid-based cytology, and PAX1/SOX1 gene methylation testing. We evaluated the diagnostic value of methylation detection in cervical cancer using DNA methylation positivity rate, sensitivity, specificity, and area under the curve (AUC), and explored its potential for triage diagnosis. PAX1/SOX1 methylation positivity rates were: control 17.1%, CIN1 22.5%, CIN2 100.0%, CIN3 90.0%, and CC 100.0%. The AUC values for PAX1 gene methylation detection in diagnosing CIN1+, CIN2+, and CIN3+ were 0.52 (95% confidence interval [CI]: 0.43-0.62), 0.88 (95% CI: 0.80-0.97), and 0.88 (95% CI: 0.75-1.00), respectively. Corresponding AUC values for SOX1 gene methylation detection were 0.47 (95% CI: 0.40-0.58), 0.80 (95% CI: 0.68-0.93), and 0.92 (95% CI: 0.811-1.00), respectively. In HPV16/18-negative patients, methylation detection showed sensitivity of 32.4% and specificity of 83.7% for CIN1+. For CIN2+ and CIN3+, sensitivity was all 100%, with specificities of 83.0% and 81.1%. Among the patients who underwent colposcopy examination, 166 cases had cytological examination results ≤ASCUS, of which 37 cases were positive for methylation, and the colposcopy referral rate was 22.29%. PAX1/SOX1 gene methylation detection exhibits strong diagnostic efficacy for cervical precancerous lesions and holds significant value in triage diagnosis.