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.

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.

[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.

Precancerous lesion determinants in women attending cervical cancer screening at public health facilities in North Shoa Zone, Amhara, Ethiopia: an unmatched case-control study.

BACKGROUND: Precancerous cervical lesions develop in the transformation zone of the cervix and progress through stages known as cervical intraepithelial neoplasia (CIN) 1, 2, and 3. If untreated, CIN2 or CIN3 can lead to cervical cancer. The determinants of cervical precancerous lesions are not well documented in Ethiopia. Therefore, this study aims to find the determinants of cervical precancerous lesions among women screened for cervical cancer at public health facilities. METHODS: A study conducted from January to April 2020 involved 216 women, consisting of 54 cases (positive for VIA during cervical cancer screening) and 162 controls (negative for VIA). It focused on women aged 30 to 49 undergoing cervical cancer screening. Multivariable logistic regression analysis assessed the link between precancerous lesions and different risk factors, considering a significance level of p < 0.05. RESULTS: Women who used oral contraceptives for a duration exceeding five years showed a nearly fivefold increase in the likelihood of developing precancerous lesions (Adjusted Odds Ratio (AOR) = 4.75; 95% CI: 1.48, 15.30). Additionally, early age at first sexual intercourse (below 15 years) elevated the odds of developing precancerous lesions fourfold (AOR = 3.77; 95% CI: 1.46, 9.69). Furthermore, women with HIV seropositive results and a prior history of sexually transmitted infections (STIs) had 3.4 times (AOR = 3.45; 95% CI: 1.29, 9.25) and 2.5 times (AOR = 2.58; 95% CI: 1.10, 6.09) higher odds of developing cervical precancerous lesions compared to their counterparts. CONCLUSION: In conclusion, women who have used oral contraceptives for over five years, started sexual activity before the age of 15 and have a history of sexually transmitted infections, including HIV, are at higher risk of developing precancerous cervical lesions. Targeted intervention strategies aimed at promoting behavioural change to prevent early sexual activity and STIs are crucial for avoiding cervical precancerous lesions. It is crucial to introduce life-course principles for female adolescents early on, acknowledging the potential to prevent and control precancerous lesions at critical stages in life, from early adolescence to adulthood, encompassing all developmental phases.

DNA flow cytometry for detection of genomic instability as a cancer precursor in the gastrointestinal tract.

One of the earliest applications of flow cytometry was the measurement of DNA content in cells. This method is based on the ability to stain DNA in a stoichiometric manner (i.e., the amount of stain is directly proportional to the amount of DNA within the cell). For more than 40years, a number of studies have consistently demonstrated the utility of DNA flow cytometry as a potential diagnostic and/or prognostic tool in patients with most epithelial tumors, including pre-invasive lesions (such as dysplasia) in the gastrointestinal tract. However, its availability as a clinical test has been limited to few medical centers due to the requirement for fresh tissue in earlier studies and perceived technical demands. However, more recent studies have successfully utilized formalin-fixed paraffin-embedded (FFPE) tissue to generate high-quality DNA content histograms, demonstrating the feasibility of this methodology. This review summarizes step-by-step methods on how to perform DNA flow cytometry using FFPE tissue and analyze DNA content histograms based on the published consensus guidelines in order to assist in the diagnosis and/or risk stratification of many different epithelial tumors, with particular emphasis on dysplasia associated with Barrett's esophagus and inflammatory bowel disease.

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.

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.

INTRODUCTION: Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis. MATERIALS AND METHODS: We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded. RESULTS: A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations. CONCLUSION: ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. METHODS: The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. RESULTS: Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). CONCLUSIONS: AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

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.

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.

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.

[Precancers of the oral mucosa: clinic, diagnostics]. / Predraki slizistoi obolochki rta: klinika i diagnostika.

OBJECTIVE: The aim of the study. Improving the efficiency of diagnosis and detailing the features of the clinic of «potentially malignant¼ diseases of the oral mucosa. MATERIALS AND METHODS: Clinical and laboratory examination of 124 patients of the department of oral mucosa diseases aged 35 to 80 years, among whom there were 75 women and 49 men, with diseases such as erythroplakia - 12 patients, verrucous leukoplakia - 52 patients, erosive form of leukoplakia - 35 patients, cheilitis Manganotti - 25 patients. Histological and immunohistochemical methods of investigation were used as diagnostics. To assess the proliferative activity of epithelial cells, the determination of the Ki-67 index was used. The synthesis of keratin 15 (K15) in epithelial layers was determined as a diagnostic criterion for the severity of neoplasia. The expression of human papillomavirus type 16 (HPV 16) antigens and p16INK4a protein in epithelial cells was studied, as well as the expression of p53 protein. RESULTS: A high prevalence of p53 mutations was observed in patients with erythroplakia. In leukoplakia, the expression of the Ki-67 protein was detected in the cell nuclei in both the basal and parabasal layers of the multilayer squamous epithelium, in 77% of cases, the expression of the p16INK4a protein in the epithelial nuclei with varying degrees of dysplastic changes was noted, and a positive reaction to HPV16 was also observed in the cell nuclei and cytoplasm of epithelial cells in the basal, parabasal and spiny epithelial layers. The appearance of K15 in the cytoplasm of cells above the basal layer with abrasive precancerous cheilitis was found in 48% of cases. CONCLUSION: To diagnose early manifestations of neoplastic processes in «potentially malignant¼ diseases of the oral mucosa, it is necessary to use both classical histological and immunohistochemical methods of investigation with various markers.

Multimodal analysis of cfDNA methylomes for early detecting esophageal squamous cell carcinoma and precancerous lesions.

Detecting early-stage esophageal squamous cell carcinoma (ESCC) and precancerous lesions is critical for improving survival. Here, we conduct whole-genome bisulfite sequencing (WGBS) on 460 cfDNA samples from patients with non-metastatic ESCC or precancerous lesions and matched healthy controls. We develop an expanded multimodal analysis (EMMA) framework to simultaneously identify cfDNA methylation, copy number variants (CNVs), and fragmentation markers in cfDNA WGBS data. cfDNA methylation markers are the earliest and most sensitive, detectable in 70% of ESCCs and 50% of precancerous lesions, and associated with molecular subtypes and tumor microenvironments. CNVs and fragmentation features show high specificity but are linked to late-stage disease. EMMA significantly improves detection rates, increasing AUCs from 0.90 to 0.99, and detects 87% of ESCCs and 62% of precancerous lesions with >95% specificity in validation cohorts. Our findings demonstrate the potential of multimodal analysis of cfDNA methylome for early detection and monitoring of molecular characteristics in ESCC.

HPV DNA Testing and Mobile Colposcopy for Cervical Precancer Screening in HIV Positive Women: A Comparison Between Two Settings in Ghana and Recommendation for Screening.

INTRODUCTION: Women living with HIV (WLHIV) have higher prevalence and persistence rates of high-risk human papillomavirus (hr-HPV) infection with a six-fold increased risk of cervical cancer. Thus, more frequent screening is recommended for WLHIV. OBJECTIVES: This retrospective descriptive cross-sectional study was conducted to investigate and compare the prevalence of hr-HPV infection and abnormal findings on mobile colposcopy in two cohorts of WLHIV following cervical screening in rural and urban settings in Ghana. METHODS: Through the mPharma 10 000 Women Initiative, WLHIV were screened via concurrent hr-HPV DNA testing (MA-6000; Sansure Biotech Inc., Hunan, China) and visual inspection (Enhanced Visual Assessment [EVA] mobile colposcope; MobileODT, Tel Aviv, Israel) by trained nurses. The women were screened while undergoing routine outpatient reviews at HIV clinics held at the Catholic Hospital, Battor (rural setting) and Tema General Hospital (urban setting), both in Ghana. RESULTS: Two-hundred and fifty-eight WLHIV were included in the analysis (rural, n = 132; urban, n = 126). The two groups were comparable in terms of age, time since HIV diagnosis, and duration of treatment for HIV. The hr-HPV prevalence rates were 53.7% (95% CI, 45.3-62.3) and 48.4% (95% CI, 39.7-57.1) among WLHIV screened in the rural vs urban settings (p-value = .388). Abnormal colposcopy findings were found in 8.5% (95% CI, 5.1-11.9) of the WLHIV, with no significant difference in detection rates between the two settings (p-value = .221). Three (13.6%) of 22 women who showed abnormal colposcopic findings underwent loop electrosurgical excision procedure (LEEP), leaving 19/22 women from both rural and urban areas with pending treatment/follow-up results, which demonstrates the difficulty faced in reaching early diagnosis and treatment, regardless of their area of residence. Histopathology following LEEP revealed CIN III in 2 WLHIV (urban setting, both hr-HPV negative) and CIN I in 1 woman in the rural setting (hr-HPV positive). CONCLUSIONS: There is a high prevalence of hr-HPV among WLHIV in both rural and urban settings in this study in Ghana. Concurrent HPV DNA testing with a visual inspection method (colposcopy/VIA) reduces loss to follow-up compared to performing HPV DNA testing as a standalone test and recalling hr-HPV positive women for follow up with a visual inspection method. Concurrent HPV DNA testing and a visual inspection method may also pick up precancerous cervical lesions that are hr-HPV negative and may be missed if HPV DNA testing is performed alone.

Cervical precancerous and cancerous lesions screening using Pap smear test at Provincial Referral Hospital of Bukavu, Eastern DR Congo: profile and recommendations to stakeholders.

Introduction: cervical cancer is a health concern worldwide. The South Kivu Province in the Eastern DR Congo is facing many cases of this disease but poorly screened and reported. The objective of this was to determine the prevalence of cell abnormalities at cervical cytology in a tertiary teaching hospital in Bukavu and their association with common risk factors of cervical cancer. Methods: a cross-sectional study was conducted on 142 women attending the Provincial Referral Hospital of Bukavu (HPGRB) from February to December 2021. Quantitative variables were described by their median following their asymmetric distributions and the qualitative variables in absolute and relative frequencies. Then the Chi-square test was used for the comparison of proportion. Results: forty-five percent of the participants had between three and five children. Twenty-two (15.5%) of the 142 patients reported to have two or more sexual partners and 17.5% reported the use of hormonal contraception. The prevalence of cell abnormalities at cervical cytology was 17% of which Low- Grade Squamous Intraepithelial Lesion (LSIL) was the most representative (12.9%). There was no statistically significant association between the common cervical risk factors and the occurrence of cell abnormalities. Conclusion: cervical pre-cancerous lesions are frequent in South Kivu province. The Pap smear test remains an early and affordable screening method and constitutes a secondary prevention strategy in women of 18 years and older in a low-income country such as DR Congo where vaccination against HPV is still hypothetic.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Condyloma and Anal Dysplasia.

Anal intraepithelial neoplasia (AIN) are precancerous lesions and are sequela of human papilloma virus (HPV) infection. AIN is classified as low-grade squamous intraepithelial lesion or high-grade squamous intraepithelial lesion. Screening with anal cytology and anoscopy should be considered for high-risk populations. Diagnosis is made through high resolution anaoscopy and biopsy. Options for treatment include ablation and several topical therapies; however, recurrence rates are high for all treatment options, and an ongoing surveillance is necessary to prevent progression to anal squamous cell carcinoma. HPV vaccination is recommended to prevent disease.

Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review.

BACKGROUND: Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS: A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS: Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION: Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.