Early Tumor Detection Research Articles

Use of thymidine kinase 1 autoantibody, thymidine kinase antigen, extracellular protein kinase A autoantibody, and C-reactive protein for early detection of malignant tumors in dogs.

Although the importance of early diagnosis of malignant tumors is a major concern, an easily accessible in-house method has not been established. To investigate the most optimal model by combining thymidine kinase 1 (TK1) autoantibody, extracellular protein kinase A (ECPKA) autoantibody, TK1 antigen, C-reactive protein (CRP), age, breed, and sex. Serum samples from 1702 dogs were collected from local animal hospitals and referral animal medical centers in Korea. TK1 autoantibody, ECPKA autoantibody, and CRP were measured using LFIA methods in serum samples of dog to design a new neoplastic index (NI) for early detection of malignant tumors in dogs. AUC of TK1 autoantibody model with TK1 antigen, CRP, age, and breed in multiple logistic regression analysis was 0.966 (TK1 autoantibody, P = .0005; TK1 antigen, P = .0003), and when the cutoff value was 0.417, the specificity was 87.1%, and sensitivity was 96.4%. Regression coefficients were 24.4, 20.5, 24.8, 10.6, respectively in TK1 autoantibody, TK1 antigen, CRP, and age, and the effect by breed (regression coefficient 2.1, 3.0) was the lowest. The same multiple logistic regression analysis on dogs with lymphoma, and AUC of TK1 autoantibody model was 0.981 (all P < .0001, TK1 antigen P = .09), when the cutoff value was 0.352, the specificity was 92.9%, and sensitivity was 93.7%. The NI including TK1 autoantibody could be useful in the screening test for both lymphoma and other tumors.

Patient-Reported Symptoms: Sentinels for Early Detection of Rare Tumors in Lynch Syndrome

Patient-Reported Symptoms: Sentinels for Early Detection of Rare Tumors in Lynch Syndrome

Grandpaternal Aging and the Risk of Primary Pediatric Brain Tumors in Grandchildren

Background and Aims: Previous research shows inconclusive evidence of a relationship between grandpaternal and parental aging and the risk of childhood brain tumors. This study aims to estimate the impact of advanced paternal and grandpaternal aging on the incidence of childhood brain tumors in Jordan. Materials and Methods: This case-control study included pediatric primary brain tumor patients and controls, matched by age and gender, ascertained from the Jordanian Cancer Registry (JCR). Collected data included patients’ diagnoses and birthdate, along with the ages of parents and paternal grandparents. Results: The study included 183 pediatric brain tumor patients and 127 controls, matched by age and gender (p&gt;0.05). Advanced grandpaternal age, defined as age at fathers' birth greater than 40 years, was present in 31.7% and 17.3% of cases and controls, respectively. Advanced grandpaternal age was associated with a 1.956-fold higher risk of developing all brain tumors (p=.012 (OR=1.956)). In participants with a grandpaternal age older than 30, advanced paternal age had a 6.56-fold increased risk of developing brain tumors (p=0.000, (OR=6.56)), an 8.4-fold increased risk of developing gliomas (p=.000, (OR=8.40)), a 4.1-fold increased risk of developing medulloblastomas (p=.045, (OR=4.1)). Grandpaternal age and advanced grandpaternal age were independent predictors for the incidence of all brain tumors, gliomas and medulloblastomas. Conclusions : Advanced grandpaternal age or a combination of advanced grandpaternal and paternal age, when combined with other risk factors, may help prevent, screen, and aid in early detection of brain tumors in the pediatric population.

EARLY DETECTION OF BRAIN TUMOR USING MRI AND TRANSFER LEARNING

Brain tumors pose significant risks to cognitive functions, making early detection crucial for improving patient survival rates. Accurate tumor detection aids neuro-oncologists in diagnosing tumor types and recommending appropriate treatments. However, manual detection is challenging, time-consuming, and prone to human error. Recently, Deep Learning (DL) models have demonstrated substantial potential in efficiently classifying large image datasets. This paper presents three novel and efficient multi-class DL architectures leveraging transfer learning to classify different brain tumors. Our primary contribution is to enhance the predictive accuracy while minimizing the usage of computational resource compared to other state-of-art models in the literature by forgoing preprocessing, segmentation, hybrid models, and augmentation techniques. Additionally, we reduce the number of FC layers to streamline computation. We conduct extensive experiments to evaluate the performance of our models using the brain tumor Figshare T1-weighted contrast-enhanced MRI dataset, comprising 3064 images from three distinct tumor types. The InceptionV3 model records a 98.36% accuracy level with five-fold cross-validation. By incorporating batch normalization and optimizing the learning rate for the Adam optimizer, the Xception model reached 99.19% accuracy. Finally, we utilize Particle Swarm Optimization (PSO) to fine-tune the learning rate of the Stochastic Gradient Descent (SGD) optimizer. The Xception model attained 98.85% accuracy. These results highlight the novelty of our approach, offering a practical solution for neuro-oncologists, particularly through the fine-tuned Xception model, which delivers early and accurate tumor detection with minimal computational resources.

Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.

Brain tumors are widely recognized as the primary cause of cancer-related mortality globally, necessitating precise detection to enhance patient survival rates. The early identification of brain tumor is presented with significant challenges in the healthcare domain, necessitating the implementation of precise and efficient diagnostic methodologies. The manual identification and analysis of extensive MRI data are presented as a challenging and laborious task, compounded by the importance of early tumor detection in reducing mortality rates. Prompt initiation of treatment hinges upon identifying the specific tumor type in patients, emphasizing the urgency for a dependable deep learning methodology for precise diagnosis. In this research, a hybrid model is presented which integrates the strengths of both transfer learning and the transformer encoder mechanism. After the performance evaluation of the efficacy of six pre-existing deep learning model, both individually and in combination, it was determined that an ensemble of three pretrained models achieved the highest accuracy. This ensemble, comprising DenseNet201, GoogleNet (InceptionV3), and InceptionResNetV2, is selected as the feature extraction framework for the transformer encoder network. The transformer encoder module integrates a Shifted Window-based Self-Attention mechanism, sequential Self-Attention, with a multilayer perceptron layer (MLP). These experiments were conducted on three publicly available research datasets for evaluation purposes. The Cheng dataset, BT-large-2c, and BT-large-4c dataset, each designed for various classification tasks with differences in sample number, planes, and contrast. The model gives consistent results on all three datasets and reaches an accuracy of 99.34%, 99.16%, and 98.62%, respectively, which are improved compared to other techniques.

Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer

Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.

Spectral CT for non-invasive evaluation of bladder cancer grade.

To investigate the potential role of dual-energy spectral computer tomography (CT) quantitative parameters in the definition of bladder cancer (BCa) pathological grading. This retrospective study evaluated the use of spectral CT imaging features for BCa. From 2021 to 2023, 63 patients with histologically-confirmed BCa diagnosis were examined at our Institution. The patients were pathologically divided, following international guidelines, into two groups: low-grade (n = 24) and high-grade urothelial carcinoma group (n = 39). The iodine concentrations (IC), the normalized iodine concentrations (NIC), and the slope of the spectrum curve (SLOPE) were calculated along with the measure of each lesion CT value on the monochromatic image from 40 to 120keV. The diagnostic performance was assessed by Receiver operator characteristic curve (ROC) analysis. The high-grade group showed significantly higher mean values of IC, SLOPE, and HU in 40 KeV monoenergetic images (VMI40 HU). AUC values for NIC, SLOPE, IC, and VMI40 HU were 0,677, 0,745, 0,745, and 0,755 respectively. In multivariate logistic regression models with backward stepwise, including all quantitative parameters, only VMI40 HU remained statistically significant to correlate with high-grade tumors. Preliminary data shows that quantitative parameters of dual-energy spectral CT can be helpful to characterize low-grade and high-grade urothelial bladder tumors. The prediction of high-grade BCa with non-invasive methods (e.g. dlCT) can aid in early detection of muscle-invasive and worse prognostic tumors that need more aggressive and timely treatments, personalizing the management on the risk of recurrence.

PATH-67. THE ROLE OF STATUS OF HER-2, ESTROGEN-RECEPTOR, BRCA 1, AND BRCA 2, IN THE INCIDENCE OF BRAIN METASTASIS IN BREAST CANCER

Abstract Breast cancer (BC) is the most diagnosed tumor in women and the second-leading cause of brain metastasis (BM), with a prevalence ranging from 10-36%. According to the Epidemiological Strategy and Medical Economics research program, advanced BC patients face an estimated 25% risk of developing BM within 2-3 years after the initial diagnosis. The frequency of BM is significantly increased by the primary tumor profile, including larger tumors, higher nuclear grade, HER-2 positivity, estrogen-receptor negativity, and triple-negative status. A gap remains in the classification of the BM site relative to primary tumor biomarkers, and the absence of multimodality treatment methodologies poses challenges to survival rates and quality of life in BC patients. This retrospective cross-sectional study at Aga Khan University Hospital identified 52 cases of brain metastasis secondary to breast cancer over a 10-year period. The median age of patients was 55 years, ranging from 32 to 78 years. Primary breast tumors were predominantly high-grade ductal carcinomas (65%), followed by lobular carcinomas (25%) and other rare histological types (10%). Secondary brain metastases were primarily located in the cerebrum (60%), cerebellum (25%), and brainstem (15%). Tumor profiles indicated a significant correlation between HER-2 positivity and the development of brain metastasis, with 70% of HER-2 positive patients presenting with extensive brain metastasis. Tumor progression varied, with a median time to brain metastasis of 2.5 years from the initial breast cancer diagnosis. Despite aggressive multimodality treatments, including surgery, radiation, and chemotherapy, the overall prognosis remained poor. The study found that early detection of HER-2 positive and triple-negative tumors could potentially delay the progression of brain metastasis through targeted therapies. Mortality rates in this cohort were high, with a median survival of 8 months following the diagnosis of brain metastasis. These findings underscore the need for improved diagnostic, preventive, and therapeutic strategies to manage brain metastasis in breast cancer patients.

Detection of Ovarian Tumors Using TVUS Imaging and Segmentation Techniques

Ovarian most cancers remains a giant health subject due to its regularly asymptomatic development until superior stages, where treatment options are confined. Early detection is critical for enhancing affected person outcomes and survival costs. This observe proposes a comprehensive technique leveraging superior photograph processing techniques for the early detection and category of ovarian tumors the usage of trans vaginal ultrasound (TVUS) snap shots. The method starts with preprocessing steps which include median filtering to do away with noise artifacts inclusive of salt and pepper noise from ultrasound photos. Ultimately, picture enhancement techniques are applied to enhance contrast and intensity, observed through binary sample evaluation to signify texture features vital for tumor identification. Photo segmentation is performed the use of okay- manner clustering and gray degree Co-prevalence Matrix (GLCM) analysis to isolate and analyze areas of interest within the ovarian photos. A Convolutional Neural community (CNN) is then hired for correct classification of ovarian tumors as regular, benign, or malignant primarily based on the extracted functions. This approach not handiest enhances the diagnostic accuracy but also minimizes affected person publicity to ionizing radiation, making it appropriate for ordinary medical applications. The proposed methodology represents a considerable advancement within the discipline of ovarian cancer detection, presenting a non-invasive and green way to evaluate ovarian health and facilitate timely intervention. Destiny research directions include refining the set of rules’s performance with large datasets and exploring additional imaging modalities for complete ovarian fitness evaluation.

3D Printed Nanosensors for Cancer Diagnosis: Advances and Future Perspective.

Cancer is the leading cause of mortality worldwide, requiring continuous advancements in diagnosis and treatment. Traditional methods often lack sensitivity and specificity, leading to the need for new methods. 3D printing has emerged as a transformative tool in cancer diagnosis, offering the potential for precise and customizable nanosensors. These advancements are critical in cancer research, aiming to improve early detection and monitoring of tumors. In current times, the usage of the 3D printing technique has been more prevalent as a flexible medium for the production of accurate and adaptable nanosensors characterized by exceptional sensitivity and specificity. The study aims to enhance early cancer diagnosis and prognosis by developing advanced 3D-printed nanosensors using 3D printing technology. The research explores various 3D printing techniques, design strategies, and functionalization strategies for cancer-specific biomarkers. The integration of these nanosensors with detection modalities like fluorescence, electrochemical, and surface-enhanced Raman spectroscopy is also evaluated. The study explores the use of inkjet printing, stereolithography, and fused deposition modeling to create nanostructures with enhanced performance. It also discusses the design and functionalization methods for targeting cancer indicators. The integration of 3D-printed nanosensors with multiple detection modalities, including fluorescence, electrochemical, and surface-enhanced Raman spectroscopy, enables rapid and reliable cancer diagnosis. The results show improved sensitivity and specificity for cancer biomarkers, enabling early detection of tumor indicators and circulating cells. The study highlights the potential of 3D-printed nanosensors to transform cancer diagnosis by enabling highly sensitive and specific detection of tumor biomarkers. It signifies a pivotal step forward in cancer diagnostics, showcasing the capacity of 3D printing technology to produce advanced nanosensors that can significantly improve early cancer detection and patient outcomes.

An oriented self-assembly biosensor with built-in error-checking for precise midkine detection in cancer diagnosis and prognosis evaluation

Midkine (MDK) is a neurotrophic growth factor highly expressed during embryogenesis, currently recognized as a multifaceted factor in cancer progression and drug resistance. MDK has demonstrated greater accuracy than existing biomarkers. Serum MDK is a valuable indicator for the non-invasive early detection of tumors. It dynamically changes following surgical tumor excision and prior to recurrence, facilitating prognosis and treatment response evaluation. However, existing methods struggle to achieve the sensitivity required for clinical applications. Herein, we developed a triple-mode biosensor with oriented self-construction and built-in error-checking for rapid, sensitive, and convenient MDK detection. The sensor construction adhered to the principle of achieving oriented and strong covalent connections to ensure high sensitivity. Biosynthesized quantum dots (BQDs) were introduced to orient antibodies, enhancing the exploration of active binding sites and significantly increasing antibody-capturing ability. To further enhance sensitivity and signal amplification, Au@Pt nanorods-Ab2 (MF-Probe) were used as multifunctional probes, incorporating an error-checking mechanism to minimize false results. Detection was feasible using an electrochemical workstation, a microplate reader, and even a mobile phone. The sensor exhibited a wide linear range from 5 fg/mL to 100 ng/mL and a low limit of detection (LOD) of 1.620 fg/mL. It accurately distinguished MDK levels in the serum of healthy donors and cancer patients. Compared to existing ELISA kits, it exhibited a lower LOD and a more sensitive response to trace MDK, suggesting it is a promising tool for cancer diagnosis and prognostic evaluation.

Positron emission tomography-magnetic resonance imaging applications in pediatric musculoskeletal tumors.

The hybrid imaging positron emission tomography/magnetic resonance (PET/MR) is an important tool in the management of pediatric oncology patients, particularly in malignant musculoskeletal pathologies, because it combines the functional and metabolic information of tumor provided by PET with the high soft-tissue contrast and the functional information offered by magnetic resonance imaging (MRI). We performed an observational retrospective study that included pediatric patients diagnosed with primary bone or soft-tissue sarcomas in the Pediatric Hematology and Oncology Unit at the HM Montepríncipe University Hospital, Boadilla del Monte, Madrid (Spain) who underwent whole-body 18F-fluorodeoxyglucose (18F-FDG) PET/MRI as a staging study and for follow-up evaluation for treatment response from September 2017 to January 2023. This study explores the protocols, the practical application of the PET/MRI technique and our clinical experience at our center in the diagnosis and follow-up of primary bone tumors and soft-tissue sarcomas in children. Seventy-one 18F-PET/MR studies were performed on 39 patients. Most of them (55%) were initial extension studies and (45%) were follow-up studies. Most of the patients studied were male (74.4%) with a median age of 15 years. Of the 39 cases, 51% were primary bone tumors while 18% were Langerhans cell histiocytosis and 21% soft tissue tumors. Of the 71 studies, five showed metastases at the initial diagnosis, three presented lymph node involvement, one with local infiltration, and one with pulmonary metastasis. PET/MR has represented a significant advancement in the evaluation of pediatric malignant neoplasms, with specific applications in musculoskeletal tumors showing clear benefits over PET/CT. The primary advantage is the reduction in ionizing radiation doses and the assessment of soft tissues, making it an excellent option for malignant musculoskeletal pathologies in pediatric patients who often require long-term follow-up. It is particularly useful for early tumor detection and functional therapy monitoring in oncology. The development of new protocols is making studies increasingly faster and better tolerated, even without the need for anesthesia. PET/MRI has shown to increase diagnostic accuracy in primary bone and soft tissue tumors, as it allows for the comprehensive evaluation of their morpho-metabolic characteristics, locoregional, and distant involvement in a single study. It is also useful in surgical planning and post-treatment follow-up.

Clinical Applications of Circulating Tumor DNA Profiling in GI Cancers.

Over the next few years, the analysis of circulating tumor DNA (ctDNA) through liquid biopsy is expected to enter clinical practice and revolutionize the approach to biomarker testing and treatment selection in GI cancers. In fact, growing evidence support the use of ctDNA testing as a noninvasive, effective, and highly specific tool for molecular profiling in GI cancers. Analysis of blood ctDNA has been investigated in multiple settings including early tumor detection, minimal residual disease evaluation, tumor diagnosis and evaluation of prognostic/predictive biomarkers for targeted treatment selection, longitudinal monitoring of treatment response, and identification of resistance mechanisms. Here, we review the clinical applications, advantages, and limitations of ctDNA profiling for precision oncology in GI cancers.

Postradiotherapy Response Assessment Using 18F-FDG PET/CT in Salivary Gland Carcinoma-A Multicenter Study.

This multicenter study investigates the efficacy of 18F-FDG PET/CT in postradiotherapy (post-RT) response evaluation in salivary gland carcinoma (SGC). We retrospectively reviewed 115 SGC patients who underwent definitive or adjuvant RT followed by 18F-FDG PET/CT between 2004 and 2021. Most tumors were parotid gland malignancies (50%). The most common histological subtypes were adenoid cystic (29%) and mucoepidermoid carcinomas (18%). The median follow-up was 65 months. Post-RT anatomic images (CT/MRI) revealed complete response (CR) in 51 patients (44%). Among 53 patients with partial response or stable disease, only 17 (32%) patients experienced locoregional recurrence, with a 5-year locoregional control rate of 69%. Post-RT 18F-FDG PET/CT documented metabolic CR in 81 patients (70%). Metabolic complete responders had significantly higher 5-year locoregional control (90% vs 43%), distant metastasis-free survival (80% vs 48%), progression-free survival (76% vs 24%), and overall survival rates (89% vs 42%) compared with non-complete responders (all P < 0.001), as confirmed in both univariate and multivariate analyses. It identified additional viable tumors in 18 cases (16%) and facilitated salvage local therapies in 7 patients (6%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of post-RT 18F-FDG PET/CT were 63%, 91%, 70%, 88%, and 84%, respectively, in predicting locoregional recurrence. 18F-FDG PET/CT showed significantly higher sensitivity (88% vs 36%, P = 0.011) in tumors with pre-RT SUVmax ≥7.39 compared with those with SUVmax <7.39. Post-RT 18F-FDG PET/CT demonstrates high negative predictive value and specificity, with metabolic CR predicting excellent outcomes. Additionally, it exhibits higher sensitivity for high-SUVmax SGC, facilitating early detection of viable tumors.

Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

Empowering Brain Tumor Diagnosis through Explainable Deep Learning

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models.

8657 Thymic Neoplasia In A Pediatric Patient With LI-Fraumeni Syndrome: A Case Report

Abstract Disclosure: M.A. Souza: None. W.M. Lins: None. D. di Matteo: None. J.L. Marcayata: None. A. Kuhn: None. H. Charcar: None. F. Ledesma: None. M.Q. Almeida: None. F. Jatene: None. B.B. Mendonca: None. M.C. Fragoso: None. Introduction: Li-Fraumeni Syndrome (LFS) is a rare autosomal dominant hereditary cancer predisposition syndrome primarily caused by pathogenic variants in the TP53 tumor suppressor gene. The individuals affected are typically young, with a cancer risk of approximately 90% in lifetime. Parents choosing to test their young children face the dilemma of when to disclose their own or the child's status and when to initiate cancer screening if LFS is detected. This process presents psychosocial challenges for both parents and affected children. Both providers and patients question whether the surveillance benefits outweigh the psychological burden of knowing LFS status and screening-related concerns, creating ambivalence toward genetic testing decisions.Case Report: We describe a 6-year-old male with Li-Fraumeni syndrome carrier of the germline pathogenic variant TP53 c.818G&amp;gt;A (p.R273H) in heterozygous (exon 8).In the family history, there are three relatives with advanced tumor cases, with the grandmother already deceased, the mother undergoing chemotherapy treatment, and the cousin being cancer-free for 10 years.The patient in question had no gestational complications. No consanguinity between parents. No signs or symptoms of hormonal hyperfunction. As proposed in the literature, Li-Fraumeni Syndrome patients should undergo frequent screening for early tumor detection, the patient began screening according to the Toronto protocol in 2018, at the age of 1 year and 7 months, through cranial and thoracic MRI, abdominal ultrasound, and annual hormonal assays. In the thoracic MRI conducted in February 2023, an enlarged and heterogeneously signaling thymus was identified, with a nodular formation (absent in previous exams), measuring about 2.5 cm. Facing suspicion of a thymoma, thymic carcinoma, or lymphoma, a biopsy guided by tomography in March 2023 was done. Histological examination was compatible with primary thymic neoplasia. During follow-up, a significant growth of the mass to 9 cm was observed. The patient underwent surgical treatment in August 2023 with resection of the mediastinal mass, and the anatomopathological examination revealed undifferentiated thymic carcinoma, followed in conjunction with the pediatric oncology team. The dilemma is about the adjuvant radiotherapy since the possibility to acquire a new tumor radio-induced such as sarcoma. We proposed to follow with MRI imaging each 3 months and if minimal signal of recurrence he will underwent radiotherapy. Conclusion: In addition to the intrinsic rarity of Li-Fraumeni Syndrome, this case draws attention to the possibility of a thymic tumor, an extremely rare neoplasm in syndrome carriers. In the literature, only scattered case reports show this association. Furthermore, this case reinforces the importance of systematic screening for investigating possible neoplasms in this susceptible population. Presentation: 6/1/2024

8657 Thymic Neoplasia in a Pediatric Patient With LI-Fraumeni Syndrome: A Case Report

Abstract Disclosure: M.A. Souza: None. W.M. Lins: None. D. di Matteo: None. J.L. Marcayata: None. A. Kuhn: None. H. Charcar: None. F. Ledesma: None. M.Q. Almeida: None. F. Jatene: None. B.B. Mendonca: None. M.C. Fragoso: None. Introduction: Li-Fraumeni Syndrome (LFS) is a rare autosomal dominant hereditary cancer predisposition syndrome primarily caused by pathogenic variants in the TP53 tumor suppressor gene. The individuals affected are typically young, with a cancer risk of approximately 90% in lifetime. Parents choosing to test their young children face the dilemma of when to disclose their own or the child's status and when to initiate cancer screening if LFS is detected. This process presents psychosocial challenges for both parents and affected children. Both providers and patients question whether the surveillance benefits outweigh the psychological burden of knowing LFS status and screening-related concerns, creating ambivalence toward genetic testing decisions.Case Report: We describe a 6-year-old male with Li-Fraumeni syndrome carrier of the germline pathogenic variant TP53 c.818G&amp;gt;A (p.R273H) in heterozygous (exon 8).In the family history, there are three relatives with advanced tumor cases, with the grandmother already deceased, the mother undergoing chemotherapy treatment, and the cousin being cancer-free for 10 years.The patient in question had no gestational complications. No consanguinity between parents. No signs or symptoms of hormonal hyperfunction. As proposed in the literature, Li-Fraumeni Syndrome patients should undergo frequent screening for early tumor detection, the patient began screening according to the Toronto protocol in 2018, at the age of 1 year and 7 months, through cranial and thoracic MRI, abdominal ultrasound, and annual hormonal assays. In the thoracic MRI conducted in February 2023, an enlarged and heterogeneously signaling thymus was identified, with a nodular formation (absent in previous exams), measuring about 2.5 cm. Facing suspicion of a thymoma, thymic carcinoma, or lymphoma, a biopsy guided by tomography in March 2023 was done. Histological examination was compatible with primary thymic neoplasia. During follow-up, a significant growth of the mass to 9 cm was observed. The patient underwent surgical treatment in August 2023 with resection of the mediastinal mass, and the anatomopathological examination revealed undifferentiated thymic carcinoma, followed in conjunction with the pediatric oncology team. The dilemma is about the adjuvant radiotherapy since the possibility to acquire a new tumor radio-induced such as sarcoma. We proposed to follow with MRI imaging each 3 months and if minimal signal of recurrence he will underwent radiotherapy. Conclusion: In addition to the intrinsic rarity of Li-Fraumeni Syndrome, this case draws attention to the possibility of a thymic tumor, an extremely rare neoplasm in syndrome carriers. In the literature, only scattered case reports show this association. Furthermore, this case reinforces the importance of systematic screening for investigating possible neoplasms in this susceptible population. Presentation: 6/1/2024

7663 Inheritable Forms of Differentiated Thyroid Cancer in a Predominantly Hispanic Pediatric Population

Abstract Disclosure: E.A. Delgado: None. A. Martinez: None. N. Solano: None. D. Baboun: None. C. Brathwaite: None. F. Alkhoury: None. C.L. Bustamante Escobar: None. A. Carrillo-Iregui: None. Background: PTEN Hamartoma Syndrome (PHTS) and DICER1 Syndrome are uncommon hereditary conditions causing benign and malignant tumors. Patients exhibiting DICER1 mutation have 5.3% likelihood of developing a tumor before reaching the age of 10, rising to 31.5% by 60. In PHTS, 75% face thyroid issues, with a 21%-38% thyroid carcinoma risk. Pediatric surveillance protocol data is scant; thus, gathering comprehensive data is vital for early tumor detection and management. Objective: Our aim is to determine the prevalence of the hereditary genetic syndromes DICER1 and PTEN associated with differentiated thyroid cancer (DTC) in a predominantly Hispanic pediatric population. This research addresses a knowledge gap in an underrepresented demographic, with the goal of improving diagnosis and management. Methods: Following approval from the Institutional Review Board, we conducted a retrospective examination of medical records from 2012 to 2023, focusing on 213 patients who underwent Ultrasound-Guided Fine Needle Aspiration (US-FNA) for thyroid nodules. Our analysis includes demographics, histopathological diagnoses, and molecular screening results. Results: Participants were predominantly Hispanic children (80%) ranging in age range from 8 to 18 years. The mean age at diagnosis was 15 years (SD± 2.49). Molecular biomarkers for thyroid disease were available in 57 of the 213 patients studied. The predominant genetic mutations identified were BRAF V600E (n=17, 30%), and RET (n=8, 14%), with PTEN showing positivity in 8.7% (n=5) and DICER1 positivity in 12.2% (n=7) of the cases. The analysis identified DTC in 64.9% (n=37) out of the total cohort with molecular markers. Subgroup analysis revealed that within the PTEN-positive subgroup, 20% developed papillary thyroid cancer, while the DICER1-positive subgroup exhibited a 14% incidence of the same cancer type. Inherited forms of thyroid cancer (DICER1 and PTEN) collectively contributed to 5.4% of DTC in our study population. Conclusion: Existing research has indicated inheritable thyroid cancer makes up around 5% of pediatric DTC. Our Hispanic-focused data aligns with prior research in broader populations, reporting low but still significant prevalence of inheritable conditions in thyroid nodules in children and adolescents, The reported cases underscore the significance of screening individuals with thyroid abnormalities for genetic mutations. Additionally, screening individuals with previously diagnosed inherited cancer syndromes for thyroid abnormalities is crucial. Future studies conducted on larger cohorts should investigate the prevalence of inheritable thyroid cancer conditions for further recommendations in surveillance strategies for children or adolescents with genetic mutations such as PTEN and DICER1. Presentation: 6/1/2024

Mucosa-like differentiation of head and neck cancer cells is inducible and drives the epigenetic loss of cell malignancy

Head and neck squamous cell carcinoma (HNSCC) is a highly malignant disease with high death rates that have remained substantially unaltered for decades. Therefore, new treatment approaches are urgently needed. Human papillomavirus-negative tumors harbor areas of terminally differentiated tissue that are characterized by cornification. Dissecting this intrinsic ability of HNSCC cells to irreversibly differentiate into non-malignant cells may have tumor-targeting potential. We modeled the cornification of HNSCC cells in a primary spheroid model and analyzed the mechanisms underlying differentiation by ATAC-seq and RNA-seq. Results were verified by immunofluorescence using human HNSCC tissue of distinct anatomical locations. HNSCC cell differentiation was accompanied by cell adhesion, proliferation stop, diminished tumor-initiating potential in immunodeficient mice, and activation of a wound-healing-associated signaling program. Small promoter accessibility increased despite overall chromatin closure. Differentiating cells upregulated KRT17 and cornification markers. Although KRT17 represents a basal stem cell marker in normal mucosa, we confirm KRT17 to represent an early differentiation marker in HNSCC tissue. Cornification was frequently found surrounding necrotic areas in human tumors, indicating an involvement of pro-inflammatory stimuli. Indeed, inflammatory mediators activated the differentiation program in primary HNSCC cells. In HNSCC tissue, distinct cell differentiation states were found to create a common tissue architecture in normal mucosa and HNSCCs. Our data demonstrate a loss of cell malignancy upon faithful HNSCC cell differentiation, indicating that targeted differentiation approaches may be therapeutically valuable. Moreover, we describe KRT17 to be a candidate biomarker for HNSCC cell differentiation and early tumor detection.