The aim of the study was to define radiobiological effects of single and fractionated low doses in normal fibroblasts in 40 patients with squamous cell carcinoma of the head and neck (HNSCC) treated with induction chemotherapy combined with low-dose fractionated radiation (LDFR) and to answer the question regarding the role of low-dose hyper-radiosensitivity (HRS) in these effects. HRS status was determined using flow cytometry-based clonogenic survival assay (cells were irradiated with doses 0.1-4 Gy of 6 MV X-rays). Radiobiological effects (cell kill, kinetics of DSB recognition and repair, chemopotentiation) of LDFR 4x0.5 Gy and a single dose of 2, 0.5 and 0.2 Gy were estimated by clonogenic, pATM and γH2AX foci assays. HRS response was demonstrated for normal fibroblasts in 6 of the 40 HNSCC patients. For all assessed biological parameters, significant interindividual differences were observed. The presence of HRS had no effect on the chemopotentiating effects of LDFR 4x0.5 Gy, which were similar to that after 2 Gy. There was also no association between HRS and the maximum number of pATM and γH2AX foci induced by single (0.2, 0.5, 2 Gy) or fractionated low doses 4x0.5 Gy. Significantly higher percentages of residual pATM and γH2AX foci observed after LDFR 4x0.5 Gy than after 2 Gy were independent of HRS. HRS is a rare finding (15%) in normal fibroblasts from HNSCC patients; therefore, it is of rather little importance in healthy late-reacting connective tissues. Moreover, the fibroblast response to single and fractionated low doses (alone or in combination with carboplatin and paclitaxel) appeared more dependent on individual radiosensitivity than on HRS.

Background: BRAF is a core component of the RAS-MAPK signaling pathway and an established oncogenic driver in several solid tumors and selected hematologic malignancies. In myeloid neoplasms, BRAF mutations are rare, and their prevalence, molecular context, and clinical significance remain incompletely defined. Available evidence is scattered across heterogeneous reports involving acute myeloid leukemia, myelodysplastic syndromes, myeloproliferative neoplasms, and overlap myelodysplastic/myeloproliferative neoplasms, with variable descriptions of mutation subtypes, co-mutational profiles, cytogenetic associations, therapeutic approaches, and clinical outcomes. To address these gaps, this review synthesizes data from the published literature up to 2025, summarizing the distribution, genetic landscape, and clinical impact of molecularly confirmed BRAF mutations across the spectrum of myeloid neoplasms. Results: Across published cohorts, BRAF mutations occurred in less than 1% of unselected myeloid neoplasms, with enrichment in chronic myelomonocytic leukemia and therapy-related or secondary acute myeloid leukemia. Both V600E and non-V600E variants were observed, typically within a complex genomic background involving ASXL1, TET2, DNMT3A, SRSF2, and RAS-pathway mutations. Acute myeloid leukemia cases showed poor prognosis, with median overall survival measured in months, whereas myelodysplastic syndromes and chronic myelomonocytic leukemia demonstrated relatively longer survival. Targeted MAPK inhibition produced hematologic responses in selected cases but rarely resulted in durable molecular clearance. Conclusions: BRAF mutations in myeloid neoplasms are rare, heterogeneous, and usually represent secondary events in clonal evolution. Although mutation clearance appears prognostically relevant, current targeted approaches provide limited durability, underscoring the need for prospective studies in this setting.

Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. This study aimed to evaluate the reliability and performance of two artificial intelligence (AI)-based image analysis systems in Ki-67 index assessment and to compare their results with expert pathologist evaluation in pulmonary neuroendocrine tumors. Methods: A total of 63 pulmonary neuroendocrine neoplasm cases, including typical carcinoid (n = 29), atypical carcinoid (n = 13), and large cell neuroendocrine carcinoma (n = 21), were retrospectively analyzed. Ki-67 proliferation indices were independently assessed by four pathologists within predefined hotspot regions, counting approximately 2000 tumor cells per case. The same regions were analyzed using two AI-based image analysis systems (Roche uPath Ki-67 and Virasoft Virasight Ki-67). Interobserver agreement among pathologists was evaluated using the intraclass correlation coefficient (ICC), and concordance between manual and AI-based assessments was assessed using Spearman's correlation and linear regression analyses. To account for potential scanner/platform effects, slides were digitized using two different whole-slide scanners (VENTANA DP^® 600 and Leica Aperio AT2), and color normalization and quality control procedures were applied prior to AI-based analysis. For clinical interpretability, Ki-67 indices were stratified into categorical groups based on tumor subtype-specific thresholds (0-<10%: low, 10-25%: intermediate, >25%: high), and agreement between manual and AI-based categorical scoring was evaluated using Cohen's kappa coefficient. Results: Among the 63 pulmonary neuroendocrine neoplasm cases, Ki-67 proliferation indices varied across tumor subtypes, with typical carcinoids showing low, atypical carcinoids intermediate, and large cell neuroendocrine carcinomas high proliferative activity. Interobserver agreement among four pathologists was excellent (ICC = 0.998, 95% CI: 0.996-0.998). Strong correlations were observed between manual Ki-67 assessments and AI-derived indices, with Spearman correlation coefficients of 0.961 (95% CI: 0.918-0.982) for Roche AI and 0.904 (95% CI: 0.821-0.949) for Virasoft AI, and 0.926 (95% CI: 0.842-0.968) between the two AI systems. Bland-Altman analyses demonstrated minimal mean differences and most cases within the 95% limits of agreement, indicating high concordance without systematic bias. Categorical agreement analysis, using subtype-specific Ki-67 thresholds (0-<10%: low; 10-25%: intermediate; >25%: high), showed excellent concordance between manual and AI-based scoring (Cohen's kappa 0.877 for Roche AI and 0.827 for Virasoft AI; p < 0.001), confirming the clinical interpretability and reproducibility of AI-based Ki-67 assessment. Conclusions: AI-based Ki-67 index assessment shows strong concordance with expert pathologist evaluation and reflects biologically relevant differences among pulmonary neuroendocrine neoplasm subtypes. These results suggest that AI-assisted Ki-67 analysis may serve as a reproducible and objective adjunct to routine diagnostic practice in pulmonary neuroendocrine tumors.

Precision oncology has been evolving rapidly, with increasing emphasis on early detection and personalized diagnostic approaches that translate into tailored treatment algorithms. The integration of molecular markers, quantitative imaging approaches and artificial intelligence (AI) in the diagnostic workflow of cutaneous squamous cell carcinoma (cSCC) has increased accuracy and has the potential to improve early detection rates in these cancers. Sun exposure is the primary etiologic factor in the development of cSCC. The primary objective of this review is to evaluate the current state and future directions of modalities and practices in diagnostic techniques for cSCC. Specifically, this review summarizes the key genetic alterations and potential molecular targets in cSCC. High-risk genetic mutations and pathways implicated in the pathogenesis of cSCC include p53, NOTCH, RAS/MAPK, cell-cycle, and adhesion pathways. This review further explores current and emerging modalities in optical imaging techniques and molecular-based diagnostic modalities in cSCC. Further, we discuss the role of radiomics and AI in the diagnostic work-up of cSCC. These techniques have the potential to enable more accurate risk models that refine conventional histopathology and guide personalized interventions. However, there are limitations to the clinical application of several of these modalities, with cost being an important driver. These challenges have been discussed in detail within this review. Nevertheless, ongoing research is focused on improving the workflow and initiating a shift in clinical practice with application of precision diagnostics as a standard of care.

Background/Objectives: The role of pre-treatment breast magnetic resonance imaging (MRI) findings and apparent diffusion coefficient (ADC) values in predicting pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NAC) has not yet been sufficiently clarified, especially in the context of molecular subtype differences. In this study, we questioned whether these imaging parameters were independent predictors of pCR. Methods: This study retrospectively explored MRI characteristics of 188 patients who underwent NAC from 2015 to 2023. The patients were divided into the pCR-positive and pCR-negative groups-the latter comprising patients with partial response (n = 61) and stable disease (n = 90)-and were classified into four molecular subtypes: Luminal A/B, HER2-enriched, and triple-negative breast cancer (TNBC). The MRI parameters included pre-chemotherapy T2-weighted signal characteristics, shape features, contrast kinetics, peritumoral edema, and ADC MIN/ADC MAX. Post-treatment ADC and ΔADC were the post-chemotherapy MRI parameters. Independent predictors were evaluated by logistic regression and discriminant performance by ROC analysis. Results: The overall pCR rate was 19.7%. In multivariate analysis, T2-weighted isointense signal (OR = 4.50), uniform tumor shape (OR = 12.83), HER2-enriched subtype (OR = 6.03), TNBC (OR = 5.15), ADC MIN (OR = 1.41), tumor size (OR = 1.28), and kinetic Type 3 pattern (OR = 3.21) were identified as independent predictors. Pre-treatment ADC MIN yielded an AUC of 0.724, while post-treatment ADC achieved 100% sensitivity and 96.7% specificity (AUC = 0.967). Conclusions: MRI morphology and ADC values may make a meaningful contribution to the prediction of pCR when evaluated in the context of molecular subtype. Post-treatment ADC demonstrated particularly strong discriminatory performance; however, external validation in multicenter cohorts is required before clinical implementation.

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for "definite" or "probable" infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics-CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted.

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight.

Background: Granular cell tumors (GCTs) are rare neoplasms that may also involve the musculoskeletal system and peripheral nerves of the extremities. In these locations, their clinical presentation, management, and outcomes remain poorly characterized. Methods: A systematic review was conducted according to PRISMA guidelines. PubMed, MEDLINE, EMBASE, and Scopus were searched for articles published between 1975 and 2025 reporting GCTs arising from the musculoskeletal system or peripheral nerves, with available data on clinical presentation and treatment. Tumor location and size, symptoms, treatment modality, and oncological outcomes (recurrence or metastasis) at the latest follow-up were extracted. Results: Forty articles describing 67 cases were included (50 females, 17 males). Tumors showed benign (47) or atypical (2) behavior in 49 cases and malignant features in 18 cases. The mean largest tumor diameter was 44 mm, and malignant lesions were significantly larger than benign ones. Thirty-one lesions were located in the lower limbs, 25 in the upper limbs, and 11 had central musculoskeletal localizations. Swelling was the most common presenting symptom (92%), followed by pain (40%). Surgical excision was performed in all patients except one, who underwent primary amputation. Adjuvant chemotherapy or radiotherapy was sporadically used in malignant cases (two cases each). Among the malignant cases with reported oncological follow-up, 44% developed distant metastases, and one (5.6%) also experienced local recurrence. Only one benign GCT recurred (2%), whereas all other non-malignant lesions remained CDF (98%). Conclusions: Although rare, GCTs should be considered in the differential diagnosis of musculoskeletal soft-tissue tumors, given their potential for malignant behavior and metastatic spread.

A 75-year-old man with newly diagnosed high-risk prostate cancer (cT3bN0M0) underwent ¹⁸F-PSMA PET/CT, which demonstrated intense tracer uptake in a left tracheal mass causing near-complete luminal obstruction, raising suspicion of a primary lung malignancy or metastatic disease. Endoscopic debulking was performed due to progressive respiratory symptoms with dyspnea. Histopathology and immunohistochemistry (p63, SMA, CK5/6 positive; PSA, NKX3.1, and AR negative, with downregulated PSMA-expression) established the diagnosis of low-grade epithelial-myoepithelial carcinoma of the trachea. Following debulking, the patient's symptoms resolved, and a watchful-waiting strategy was adopted for the tracheal tumor, while curative-intent therapy for prostate cancer continued. This case highlights that ¹⁸F-PSMA PET/CT may reveal rare, intensely PSMA-avid non-prostatic neoplasms and underscores the importance of recognizing atypical uptake patterns to avoid misinterpretation during prostate cancer staging.

Background and Clinical Significance: Plexiform fibromyxoma (PFM) is a rare benign gastric mesenchymal neoplasm characterized by multinodular plexiform growth of bland spindle cells in a myxoid or fibromyxoid stroma. We report a case of the cellular form of PFM with limited myxoid stroma and aberrant KIT expression, resulting in diagnostic difficulty by biopsy. Case Presentation: A 59-year-old woman presented with a slowly enlarging 15 mm gastric antral submucosal tumor. A resected specimen by laparoscopic and endoscopic cooperative surgery revealed spindle cell proliferation forming plexiform nodules with a myxoid background in limited areas. Positive immunoreactivity of a subset of spindle cells for KIT suggested a diagnosis of gastrointestinal stromal tumor (GIST), although DOG1 was negative. In addition, diffuse staining for CD10, smooth muscle actin, and D2-40 was confusing. MALAT1::GLI1 fusion was detected by next-generation sequencing analysis. Consequently, a diagnosis of PFM was established. Conclusions: This case expands the morphologic and immunophenotypic spectrum of PFM and indicates the possible diagnostic utility and biological significance of D2-40 expression. Although molecular confirmation of MALAT1::GLI1 fusion is definitive for the diagnosis of PFM, the findings of the present case may aid diagnosis in challenging cases that mimic GIST.