Diagnosis Of Tumors Research Articles

Myeloid sarcomas with CBFA2T3 : GLIS2 fusion: clinicopathologic characterization of 4 cases mimicking small round cell tumors.

Acute myeloid leukemia with CBFA2T3::GLIS2 fusion can initially present as extramedullary lesions (myeloid sarcoma), leading to a misdiagnosis of nonhematologic pediatric solid tumors. We characterized the clinicopathologic features of 4 cases of CBFA2T3::GLIS2 fusion-positive myeloid sarcoma in pediatric patients where the sarcoma presented either without leukemic involvement (isolated myeloid sarcoma; 3/4 [75%]) or had concurrent leukemic disease (1/4 [25%]). All cases mimicked nonhematopoietic tumors at morphologic and immunophenotypic levels, so the initial evaluation did not raise suspicion for acute myeloid leukemia/myeloid sarcoma. After extensive workup, however, including molecular studies, the diagnosis of myeloid sarcoma with CBFA2T3::GLIS2 fusion was rendered. This study highlights the need for a high suspicion index of GLIS2-rearranged myeloid sarcoma in the differential diagnosis of pediatric small round cell tumors in tissue biopsies and the application of adequate workup to avoid misdiagnosing this entity.

Role of Immunohistochemistry in the Diagnosis of Pilomatrical Tumors.

Pilomatrical skin tumors harbor mutations in CTNNB1, which encodes for β-catenin, a downstream effector of the Wnt signaling pathway responsible for the differentiation, proliferation, and adhesion of epithelial stem cells. Therefore, downstream molecules, such as CDX2, LEF-1, and SATB2, in the Wnt signaling pathway could be useful diagnostic markers. Here, we sought to investigate the potential of immunohistochemistry (IHC) to differentiate between pilomatricoma and pilomatrical carcinoma, as well as from other cutaneous adnexal tumors. We studied 88 cases of cutaneous tumors (14 pilomatrical carcinomas, 18 pilomatricomas, 13 basal cell carcinomas, 12 squamous cell carcinomas, 12 sebaceous carcinomas, 10 Merkel cell carcinomas, 7 trichoblastomas, and 2 hidradenocarcinomas) using a broad panel of IHC markers: β-catenin, SATB2, CDX2, LEF1, Ber-EP4, and PRAME. Pilomatricoma and pilomatrical carcinoma displayed >75% nuclear staining for β-catenin. CDX2 also strongly stained pilomatrical tumors; however, the staining distribution was limited in pilomatricoma and more widespread in pilomatrical carcinoma. But, overall, it was less than β-catenin. SATB2 and Ber-EP4 expressions were noted only in a subset of both pilomatrical carcinoma and pilomatricoma, whereas LEF-1 showed strong, diffuse nuclear positivity in both pilomatricoma and pilomatrical carcinoma. Among the IHC markers evaluated, none could distinguish between pilomatricoma and pilomatrical carcinoma. However, the combined use of β-catenin with CDX2 markers may assist in not only confirming the pilomatrical nature of the proliferation but also in differentiating benign from malignant cases when there is a significant presence of CDX2 staining. Despite these findings, the diagnosis should continue to primarily depend on a thorough histopathologic examination.

Programmable intelligent DNA nanoreactors (iDNRs) for in vivo tumor diagnosis and therapy.

With the rapid advancement of DNA technology, intelligent DNA nanoreactors (iDNRs) have emerged as sophisticated tools that harness the structural versatility and programmability of DNA. Due to their structural and functional programmability, iDNRs play an important and unique role in in vivo tumor diagnosis and therapy. This review provides an overview of the structural design methods for iDNRs based on advanced DNA technology, including enzymatic reaction-mediated and enzyme-free strategies. This review also focuses on how iDNRs achieve intelligence through functional design, as well as the applications of iDNRs for in vivo tumor diagnosis and therapy. In summary, this review summarizes current advances in iDNRs technology, discusses existing challenges, and proposes future directions for expanding their applications, which are expected to provide insights into the development of the field of in vivo tumor diagnostics and targeted therapies.

Development of Prediction Models to Detect the Presence of MGMT Promoter Methylation for Prognosis of Brain Tumor

Methylation of the MGMT promoter is a molecular marker of predictive relevance in brain tumors. Methylation of the MGMT promoter is related to an improved prognosis and may influence therapy decisions. According to the most recent Malaysian National Cancer Registry Report (MNCRR) for 2012–2016, there were 2097 cases of brain tumors in Malaysia overall, with 1117 cases involving men and 908 cases involving women. This research aims to create prediction models for detecting MGMT promoter methylation in brain tumors. So, a dataset from the BraTS (Brain Tumor Segmentation) challenge, which included MRI scans and clinical data for patients with brain tumors, was utilized and explored using Exploratory Data Analysis (EDA) and data visualization techniques. The dataset has 306 methylation cases identified as “1” and 276 unmethylated cases identified as “0”. The average number of scans of train data for modality per patient is between 127 and 171, which provides a wealth of information for pattern learning while the average number of scans of test data for modality per patient is between 124 and 165. In both sets, FLAIR has the least number of files while T2w has the highest number of files among them. Two models of deep learning approaches, ResNet50 and EfficientNetV2, were used to construct and form the prediction models. Various criteria were employed to evaluate the performance of the models. The T2w modality consistently achieved the highest validation accuracy, precision, recall, and F1-score for both the ResNet50 and EfficientNetV2 models. Specifically, the T2w modality achieved a validation accuracy of 0.9453 and a validation loss of 0.2417 for ResNet50. Furthermore, the brain tumor diagnosis interface utilized DICOM images from MRI scans to identify MGMTp methylation status, aiding in therapy effectiveness prediction. Pre-trained ResNet50 model on T2w images was used for classification. The interface displayed the original MRI image, predicted state, and treatment effectiveness indication, while promptly notifying users of invalid or inadequate data for accurate analysis.

Saliva metabolomics: concepts and applications in oral disorders.

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies. This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages. Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline. Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories. Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images

MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

Evaluating Endoscopic Ultrasound-Guided Tissue Acquisition for the Diagnosis of Small Pancreatic Neuroendocrine Neoplasms

Objectives: Although small hypervascular tumors are suspected to be pancreatic neuroendocrine tumors (p-NENs), their diagnosis and treatment are challenging. This study evaluates the usefulness of endoscopic ultrasound-guided tissue acquisition (EUS-TA) for the diagnosis of small p-NENs. Methods: All p-NEN lesions that underwent EUS-TA at our hospital between April 2018 and December 2023 were retrospectively analyzed. The diagnostic sensitivity of EUS-TA and the concordance rate of grading with EUS-TA and surgical specimens were examined. The lesions were grouped by size. Results: The diagnostic sensitivity of EUS-TA was analyzed for 82 lesions, of which 44 were compared with postoperative specimens for grading. The definitive diagnosis was neuroendocrine tumor (NET) in 75 lesions, neuroendocrine carcinoma in five lesions, and mixed neuroendocrine non-neuroendocrine neoplasm in two lesions. Thirty tumors were ≤ 10 mm, 30 were 10–20 mm, and 22 were > 20 mm, and the diagnostic sensitivities were 96.7%, 96.7%, and 90.9%, respectively. The concordance rates for grading were 94.4%, 82.4%, and 77.8% for tumors ≤ 10 mm, 10–20 mm, and ≥ 20 mm, respectively, with Cohen’s kappa coefficients of 0.64, 0.48, and 0.40, respectively. Conclusions: EUS-TA showed adequate diagnostic sensitivity and grading agreement for p-NENs of all sizes, allowing for the determination of the appropriate treatment.

Nanozyme-Based Pump-free Microfluidic Chip for Colorectal Cancer Diagnosis via Circulating Cancer Stem Cell Detection.

Circulating cancer stem cells (CCSCs) are subpopulations of cancer cells with high tumorigenicity, chemoresistance, and metastatic potential, which are also major drivers of disease progression. Herein, to achieve the prediction of tumor diagnosis and progression in colorectal cancer (CRC), a new, automated, and portable lateral displacement patterned pump-free (LP) microfluidic chip (LP-chip) with the CoPt3 nanozyme was established for CCSC capture and detection in peripheral blood and feces samples ex vivo. In this design, CoPt3@HA probes with functions of magnetic separation and colorimetric signal transduction by peroxidase-mimicking activity were applied for the capture of CCSCs and signal output in clinical samples. The generated colors of polydopamine (PDA) were quantifiable through the smartphone APP and visualizable by the naked eye in the test line (T line) and control line (C line) of the LP-chip. In the optimal experimental conditions, the CCSC concentration was sensitive to change in the range 0-105 cells mL-1, with a detection limit of 3 cells mL-1 (S/N = 3). Preliminary studies of clinical samples suggest that the platform has the potential for prediction of colorectal cancer progression and poor prognosis. Overall, the LP-chip provides potential strategies for timely diagnosis, therapeutic monitoring, and recurrence prediction to improve home-based patient care.

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.

Three‐Pronged Induction of Tumor Calcification Synergistic Calcium Immunotherapy Enables Diagnosis and Cure of Cancer

AbstractTumor calcification is considered to have the superiority of integrating tumor diagnosis and treatment, and it also has been found to be a predictor of favorable prognosis. However, selecting suitable nanocarriers and therapeutic drugs to maximize the tumor calcification efficiency are key factors for improving calcification‐medicated theranostics. This study performs a multi‐channel calcification nanoinitiator (CalNI, i.e., BP‐Cur‐Ca@PKLAK) by loading curcumin (Cur) and depositing Ca2+ on black phosphorus (BP) and combined carrying mitochondria‐targeting cytotoxic peptide KLAK to boost a triple‐induced tumor calcification. CalNI preferentially accumulates inside tumor mitochondria and induces multilevel calcification promotion by the three combined effects of (i) the degradation of BP into PO43− provides the phosphorus source required for calcification; (ii) burst of calcium ions can directly induce cellular calcium overload; (iii) Cur can inhibit calcium efflux thus further aggravate intracellular calcium stress. Moreover, KLAK exhibited enhanced mitochondrial synergistic toxic effects. Notably, the potential function of CalNI on the regulation of macrophage polarization could completely reverse the immunosuppressive microenvironment, and activate the initiation of T cell‐mediated immune response. Therefore, this three‐pronged CalNI showed a huge promise to be an alternative to conventional oncology therapy, which presents a more satisfactory therapeutic efficiency than any kind of single induction mode.

8501 Adrenal Incidentaloma In A Pregnant Woman Diagnosed With Preeclampsia

Abstract Disclosure: V.P. Vargas-Abonce: None. I.M. Hernandez-Riveros: None. J. Vasquez-Vasquez: None. F.D. Martínez-Sánchez: None. J.L. Hernandez-Castillo: None. A. Pretel Gutiérrez: None. T.G. Jaramillo-Salazar: None. M.E. Rodriguez García: None. E.K. Tenorio-Aguirre: None. Introduction: An adrenal incidentaloma is a tumor detected on imaging conducted for reasons other than suspected adrenal disease. Whether an incidentaloma should be addressed during pregnancy has not been discussed since this condition is very uncommon. The correct diagnosis and treatment of a functional adrenal tumor has significant implications for maternal-fetal morbidity and mortality. Case Presentation: A 25-year-old woman at 37 weeks of gestation presented to the obstetric emergency department with a headache accompanied by tinnitus and dizziness. Arterial blood pressure at admission was 180/120 mmHg, and biochemical urinary analysis showed proteinuria. A diagnosis of preeclampsia was made, and an emergency cesarean section was performed due to fetal bradycardia. A live single neonate was delivered with an Apgar score of 5/9 at 1 and 5 minutes respectively. During surgery, the patient's arterial blood pressure persisted at 190/110 mmHg despite treatment with 20 mg intravenous hydralazine. At her admission to the intensive care unit, an esmolol infusion was indicated, but the patient further presented tachypnea and tachycardia. A chest Angio tomography ruled out pulmonary thromboembolism, but imaging of the abdomen revealed a right adrenal mass of 6 cm with a heterogeneous enhancement in the arterial phase. Considering the clinical evolution and the findings in tomography, a pheochromocytoma was suspected. The antihypertensive treatment was switched from labetalol to prazosin, achieving control of blood pressure and clinical improvement. The patient was discharged after five days to continue the approach of an incidentaloma. Contrast-enhanced abdominal MRI reported a 9 cm right adrenal mass with a heterogeneous center and hypointense areas suggestive of necrosis. Plasma-free metanephrine was reported at 128 pg/mL (reference value <100 pg/mL) and normetanephrine at 3976 pg/mL (reference value <126 pg/mL). A right adrenalectomy was scheduled. Conclusion: In the workup of an incidentaloma, suggestive characteristics of malignancy in TC, and functional evaluation guided by the patient's clinical manifestations must be addressed concurrently. In this case, the presence of an adrenal incidentaloma larger than 4 cm with >10 HU in a pregnant woman diagnosed with preeclampsia who had an inadequate response to the standard treatment led to the consideration of a probable pheochromocytoma as a secondary cause of hypertension, which influenced the change of antihypertensive treatment to alpha-adrenergic blockade, followed by patient clinical improvement. Although the presence of a pheochromocytoma is much less common than preeclampsia as a cause of hypertension in pregnancy, a misdiagnosis can increase maternal morbidity and mortality by up to 15%. After adequate treatment of the obstetric emergency, the approach to incidentaloma in pregnancy is recommended. Presentation: 6/3/2024

7211 Clinical and Molecular Characterization Of An Aldosterone-Producing Adenoma Harboring Double Somatic Mutations In CTNNB1 and GNA11

Abstract Disclosure: A. Blinder: None. K. Nanba: Grant Recipient; Self; AstraZeneca. A. Udager: None. Y. Hirokawa: None. T. Miura: None. H. Okuno: None. K. Moriyoshi: None. Y. Yamazaki: None. H. Sasano: None. A. Yasoda: None. N. Satoh-Asahara: None. W.E. Rainey: None. T. Tagami: None. Background: Somatic activating mutations in CTNNB1, encoding β-catenin, have been detected in a small subset of aldosterone-producing adenomas (APAs). As a possible pathogenesis, an association of CTNNB1-mutated APA with pregnancy and menopause has been proposed. A recent study reported double somatic mutations in CTNNB1 and GNA11/Q, encoding guanine nucleotide-binding protein subunits alpha-11 and alpha-Q, in APA. However, due to its rare incidence, clinical and molecular features of APA associated with these mutations have not been well characterized. Herein, we report a case of APA harboring somatic CTNNB1 and GNA11 mutations. Clinical Case: A 46-year-old Japanese woman presented with hypertension and hypokalemia. She had two pregnancies in the past but had no history of pregnancy-induced hypertension. She had regular menstrual cycles at presentation. Laboratory testing showed elevated plasma aldosterone concentration with suppressed plasma renin activity. She was diagnosed as having primary aldosteronism based on the results of a captopril challenge test. Adrenal computed tomography revealed a 2 cm right adrenal mass. Adrenal venous sampling indicated excess aldosterone production from the right adrenal gland. She underwent right laparoscopic adrenalectomy. Histologic diagnosis of the resected tumor was adrenocortical adenoma based on the criteria of Weiss. Notably, Ki-67 labeling index was high (6% at hotspots). After surgery, her blood pressure and serum potassium both normalized. According to the primary aldosteronism surgical outcome (PASO) study criteria, PA remained clinically and biochemically cured at follow-up 2.5 years after surgery. Results: Immunohistochemistry (IHC) showed high and diffuse expression of aldosterone synthase (CYP11B2) within the tumor, whereas immunoreactivity of 17α-hydroxylase (CYP17A1) and 11β-hydroxylase (CYP11B1), both required for cortisol biosynthesis, was markedly low. VSNL1, a marker for the zona glomerulosa (ZG) where physiologic aldosterone biosynthesis occurs, was highly expressed within the tumor. CYP11B2 IHC-guided tumor capture followed by targeted next-generation sequencing identified double somatic CTNNB1 (p.D32Y) and GNA11 (p.Q209H) mutations. Quantitative real-time RT-PCR revealed high tumor expression of LHCGR (LH receptor) and GNRHR (GNRH receptor) mRNA levels compared with those in adjacent normal adrenal (148-fold and 56-fold, respectively). Immunofluorescence staining of the tumor revealed the presence of activated β-catenin, consistent with features of the normal ZG. Conclusions: Our study indicates that an APA with double somatic mutations in CTNNB1 and GNA11 has characteristics of the ZG and could occur in adults with no clear association with pregnancy or menopause. Presentation: 6/3/2024

8390 It's not Cancer: Establishing the Diagnosis of Hyaline Trabecular Tumor

Abstract Disclosure: R.A. Zielinski: None. M. Khalifa: None. M. Dillon: None. M. Kaur: None. Background: Hyaline trabecular tumor (HTT) is a rare benign follicular thyroid neoplasm characterized by extensive intra-trabecular hyaline stromal material. Resemblances to other follicular neoplasms create difficulty in distinguishing HTT from its more nefarious counterparts such as medullary thyroid cancer (MTC) and papillary thyroid cancer (PTC).Clinical Case:Our patient is a 56-year-old female with Hashimoto’s thyroiditis diagnosed in her twenties who has been on different strengths of levothyroxine based on her fluctuating TSH. A thyroid ultrasound done to evaluate left-sided neck fullness on physical exam showed a solid, hypoechoic 2.3 cm x 1.4 x 0.9 cm left middle thyroid nodule without calcifications (TI-RADS 4) and benign-appearing lymph nodes, the largest of which was 1.5 cm at level 1B. Cytology from a thyroid FNA biopsy was suspicious for PTC, Bethesda category IV. A thyrosure genetic panel was negative for genetic mutations. FNA biopsy of the 1.5 cm lymph node was negative for malignancy. The patient was referred for thyroid lobectomy but instead elected to undergo total thyroidectomy with level VI lymph node dissection. Pathology revealed a 2.2 cm HTT in the left lobe, scattered psammoma bodies in adjacent soft tissue, and a 0.2 cm multifocal papillary microcarcinoma in the right lobe. The microcarcinoma had negative margins, no lymphatic or vascular invasion, and 7/7 benign level VI lymph nodes; staging it as pT1aN0. Immunohistochemistry staining was positive for TTF-1 and thyroglobulin and negative for calcitonin, chromogranin, and synaptophysin. Post-thyroidectomy she was started on Tirosint 175 mcg daily with good response. No I-131 treatment was pursued due to the microcarcinoma being categorized as low risk.Discussion and Conclusion: HTT is a benign neoplasm and management is usually conservative with surgical excision. The diagnosis of HTT by cytology alone is challenging due to the presence of nuclear clearing, grooves and intranuclear pseudo-inclusions that may be mistaken for PTC or MTC. Cell membrane immunoreactivity of MIB1 monoclonal to Ki67 and the molecular identification of the novel PAX8-GLIS1 or PAX8-GLIS3 fusions may aid in making the correct diagnosis.

β-galactosidase instructed in situ self-assembly fluorogenic probe for prolonged and accurate imaging of ovarian tumor

Fluorescent probes are essential for optical imaging and have been extensively employed for precise cancer diagnosis studies. β-galactosidase (β-gal) serves as a primary biomarker for ovarian cancer and has been utilized to develop imaging probes for accurate tumor diagnosis. However, traditional small molecular probes have limitations in terms of rapid diffusion and metabolic clearance from the target lesion, resulting in a short imaging window and compromised tumor-to-background ratios (TBR). Herein, we integrated an enzyme-instructed in situ self-assembly strategy to construct Gal-IRFF, a small molecule-based activatable near-infrared (NIR) fluorogenic probe. Upon cleavage by endogenous β-gal overexpressed in ovarian cancer cells, IRFF exhibited enhanced NIR fluorescence signals and self-assembled into nanoparticles through intermolecular interactions of the Phe-Phe (FF) dipeptide moiety, which facilitated probe accumulation and retention within the tumor lesion. Compared with the small molecule probe Gal-IR, our proposed self-assembly probe Gal-IRFF demonstrated a lower limit of detection (LOD) towards β-gal and showed remarkable improvements in distribution and retention time within SKOV3 cells in vitro and tumors in vivo, thereby providing a long-term imaging window for real-time monitoring β-gal levels in ovarian tumors. Therefore, this study highlights the potential of an enzyme-instructed self-assembly fluorogenic probe design approach for achieving precise tumor diagnosis in vivo.

Olfactory Receptors and Tumorigenesis: Implications for Diagnosis and Targeted Therapy.

Olfactory receptors (ORs) are a class of G protein-coupled receptors (GPCR) widely distributed in olfactory sensory neurons and various non-olfactory tissues, serving significant physiological and pathological functions in the human body. Increasing evidence reveals the heightened expression of olfactory receptors in tumorous tissues and cells alongside normal tissues. Olfactory receptors have demonstrated influence over tumor cell proliferation and metastasis, establishing a close relationship with tumor initiation and progression. This review highlights the specific molecular actions and signaling pathways of olfactory receptors in the development of human tumors. The potential for precise tumor diagnosis and targeted therapy through therapeutic targeting of olfactory receptors as an adjunct anticancer treatment strategy is being considered.

Unlocking delays: revealing barriers to early diagnosis of childhood central nervous system tumors in an upper-middle-income country.

Childhood central nervous system (CNS) tumors tend to have a longer time interval until diagnosis than other pediatric malignancies. The aim is to describe the time to diagnosis among Brazilian pediatric patients treated at a tertiary center and explore associated factors. Cross-sectional study; application of questionnaires to parents of children with CNS tumors during outpatient visit or inpatient care. One hundred parents participated between August and November 2023. The median age of the children at diagnosis was 7.2years old. Low-grade glioma (LGG) was the most common tumor type (37%), followed by medulloblastoma (24%). The most frequent symptoms were morning and/or persistent vomiting and headache. The mean prediagnostic symptomatic interval (PSI) was 150days. The mean parental interval was shorter than the medical (58.1days vs 92.8days). LGGs and tumors located in the central area had longer intervals to diagnosis than other tumors (296 vs 54days) (p = 0.005) and (206 vs 155days) (p = 0.007), respectively. Despite 81% of the patients undergoing pediatric routine follow-up, 87% of them had been diagnosed at an emergency department. Children attended by the same physician had a shorter mean interval (18.2 vs 88.3days) than those assisted by different professionals (p = 0.015). The mean time for referral to our specialized center was 23days. This study is a crucial step in recognizing barriers to early diagnosis of CNS tumors in a middle-income country as low awareness of signs/symptoms by parents and health professionals, aiming to provide opportunities for intervention strategies to reduce the time to diagnosis.

ZFHX2-AS1 interacts with DKC1 to regulate ARHGAP5 pseudouridylation and suppress ovarian cancer progression

Ovarian cancer (OCa) remains a highly lethal disease, largely due to late-stage diagnosis and limited treatment options for recurrent metastatic tumors. Long non-coding RNAs (lncRNAs) have been recognized as key regulators of cancer hallmarks, yet their specific roles in driving OCa progression are not fully understood. In this study, we employed an integrated approach combining clinical correlation, functional assays, and mechanistic investigations to reveal that lncRNA ZFHX2-AS1 is significantly downregulated in OCa tissues and cells, with its reduced expression associated with poor clinical outcomes. Using in vitro and in vivo models, we demonstrated that overexpression of ZFHX2-AS1 suppresses OCa cell proliferation, migration and invasion, whereas ZFHX2-AS1 knockdown enhances these malignant phenotypes. Mechanistically, we defined that ZFHX2-AS1 interacts with and attenuates the enzymatic activity of the pseudouridine synthase DKC1, thereby reducing pseudouridylation and stabilizing the oncogenic ARHGAP5 mRNA. Re-expression of ARHGAP5 could partially reverse the tumor-suppressive effects of ZFHX2-AS1. Further, we found that ARHGAP5 promotes epithelial-mesenchymal transition (EMT) by regulating Rho GTPases activities, and that ZFHX2-AS1 inhibits EMT in OCa by downregulating ARHGAP5 expression and suppressing the Rho GTPase signaling pathway. Taken together, our findings identify ZFHX2-AS1 as a potent tumor suppressor in OCa, acting through the modulation of DKC1-mediated pseudouridylation of ARHGAP5 and the inhibition of the Rho GTPase pathway, thus offering a potential therapeutic target for combating OCa progression.

Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition

The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.

Associations between central nervous system tumor diagnosis stage and survival and Medicaid enrollment among children, adolescents, and young adults

Abstract Background Medicaid enrollment has been associated with disparities in younger cancer patient survival. To further understand this association for central nervous system (CNS) tumor patients, we used Surveillance, Epidemiology, and End Results (SEER)-Medicaid-linked data to examine associations between Medicaid enrollment and enrollment timing and (1) diagnosis stage, and (2) CNS tumor death. Methods Individuals diagnosed with a first malignant primary CNS tumor between 0 and 39 years from 2006 to 2013 were included. Medicaid enrollment was first classified as enrolled versus not enrolled with those enrolled further classified as having continuous, discontinuous (at diagnosis or other discontinuous), or other enrollment. We used logistic and Cox Proportional Hazards regression stratified by age to calculate adjusted odds ratios (ORs) and hazard ratios (HRs) for those 0–14 and 15–39 years. Results Among 10 107 CNS tumor patients, we found significantly higher odds of regional/distant versus in situ/localized stage diagnoses for those with other discontinuous (OR0–14 = 1.50, 95% CI: 1.15–1.95) and at diagnosis (OR15–39 = 1.41, 95% CI: 1.11–1.78) Medicaid enrollment versus those not enrolled. Those enrolled versus not enrolled in Medicaid had a higher hazard of CNS tumor death for both age groups (HR0–14 = 1.60 95% CI: 1.37–1.86; HR15–39 = 1.50, 95% CI: 1.39–1.62) with the highest hazards for those enrolled at diagnosis (HR0–14 = 1.83, 95% CI: 1.51–2.22; HR15–39 = 1.93, 95% CI: 1.77–2.10). Conclusions Medicaid enrollment is associated with a higher risk of CNS tumor death with an almost 2-fold higher risk for young CNS tumor patients enrolled at diagnosis. These results support the critical need for consistent health insurance coverage for young CNS tumor patients.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

BackgroundLiver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.MethodsWe designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness.ResultsThe H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.ConclusionThe proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.