Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
The occurrence of osseous, fibrous, and fibro-osseous lesions in the jaw bones might pose challenges for accurate diagnosis and the selection of the best therapeutic approach. Certain radiolucent, radiopaque, or mixed-origin lesions can look very similar to other bone lesions, because of the stages of their growth, calcification, maturation, and possible local factors affecting the lesion. Ossifying fibroma (OsF, OF) is a type of fibro-osseous lesion, whose radiological characteristics might sometimes be uncertain. It may appear on classic radiographs and cone beam computed tomography as a radiolucent/ radiopaque lesion with calcification bodies or a shape with a cloud-like appearance. The appearance is mostly related to the lesion’s maturation level, calcification stage, and number of fibrous elements. Diagnosis might be challenging. Its histopathological evaluation reveals a combination of mineralized and fibrous connective tissues in the mass. From a radiological point of view, because of the tumor’s various stages of bone remodeling, formation, and resorption, diagnosis might be troublesome. Different diagnoses should include cemento-osseous dysplasia, fibrous dysplasia, or cementoblastoma. A biopsy could provide an accurate histopathological examination, improving diagnosis and influencing later surgical approaches. Regardless of the final specimen evaluation, surgery is the treatment of choice. The authors would like to present the correlation between radiological and histopathological data in tumor treatment outcomes....
Background: Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic characteristics. We aimed to develop a machine learning-based diagnostic model using CT-derived radiomic features to classify the five most common renal tumor subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), angiomyolipoma (AML), and oncocytoma. Methods: A total of 499 patients with pathologically confirmed renal tumors who underwent preoperative contrast-enhanced CT and nephrectomy were retrospectively analyzed. Results: We extracted and analyzed radiomic features from 1548 multi-phase CT scans from 499 patients, focusing on fat-poor tumors. Five machine learning classifiers including Linear SVM, Rbf SVM, Random Forest, and XGBoost were involved. Among the models, XGBoost showed the best classification performance, with an average AUPRC: mean = 0.757, standard error = 0.033 and a renal angiomyolipoma-specific AU-ROC: mean = 0.824, standard error = 0.023. These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. Conclusions: This study demonstrates the effectiveness of radiomics-based machine learning in classifying renal tumor subtypes and highlights the potential of AI in medical imaging. The findings, particularly the utility of single-phase CT and feature optimization, offer valuable insights for future precision medicine approaches. Such methods may support more personalized diagnosis and treatment planning in renal oncology....
Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vspCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level....
Background/Objectives: To use natural language processing (NLP) to extract large-scale data from the CT radiology reports of patients with advanced melanoma treated with immunotherapy and to determine whether liver metastases affect survival. Methods: Patient criteria (M1 disease subclassified into M1a, M1b, or M1c) as well as alternative criteria (M1 with advanced melanoma, imaged with CT chest, abdomen, and pelvis from July 2014–March 2019) were included retrospectively. NLP was used to identify metastases from CT reports, and then patients were classified according to American Joint Committee on Cancer (AJCC) staging disease subclassified into M1L+ or M1L−, indicating whether liver metastases were present or not). Statistical analysis included constructing Kaplan–Meier survival curves and calculating hazard ratios (HRs). Results: 2239 patients were included (mean age, 63 years). Whether using AJCC or alternative criteria, overall survival (OS) was poorest for M1L+ (entire cohort median OS, 0.69 years [95% CI: 0.60–0.82]; immunotherapy cohort median OS, 1.4 years [95% CI: 0.92–2.0]) compared to M1L− (entire cohort median OS, 1.8 years [95% CI: 1.4–2.2]; immunotherapy cohort median OS; M1L−, 2.9 years [95% CI: 2.3–3.9]). The median HR for M1L+ (median HR, 5.35 [95% CI: 4.59–6.24]) was higher than that for M0 (p < 0.001). The median HR for M1L+ (median HR, 2.13 [95% CI: 1.65–2.64]) was higher than that for M0 (p < 0.01). Conclusions: Patients with advanced melanoma, particularly those with liver metastases, demonstrated inferior survival, even when treated with immunotherapy....
Objective: To systematically evaluate the effectiveness and safety of radiofrequency ablation (RFA) for managing pain caused by spinal metastases. This review aimed to consolidate evidence on RFA’s analgesic efficacy and associated risks to inform clinical practice in palliative cancer care. Methods: A systematic review adhering to PRISMA guidelines was conducted. Databases were searched for studies evaluating RFA for spinal metastases pain. Inclusion criteria specified: randomized or nonrandomized studies (prospective/retrospective); ≥3 adult patients; RFA used alone or combined with other treatments; reported pre- and post-RFA pain assessments; English language publication. Data extracted included patient demographics, primary tumor type, lesion location, pain scores (e.g., NRS/VAS), and complications. Pain response was assessed using definitions including the International Consensus Pain Response Endpoints (ICPRE) and definitions for moderate (≥2-point reduction) and high (≥4-point reduction) effectiveness. Results: This review included 33 studies, totaling 1336 patients (53.7% female) and 1787 treated lesions. The majority (85%) of studies reported highly effective pain management (≥4-point pain score reduction). The remaining 15% showed moderate effectiveness (≥2-point reduction). All studies reported achieving at least a partial pain response per ICPRE criteria. Mean pain scores decreased significantly from baseline (7.56/10) within 24–72 h (3.65) and remained low at 4 weeks (2.99), 12 weeks, and 24 weeks (both 2.70). Common primary cancers were lung (27.6%), breast (26.2%), and genitourinary (11.3%). Lesions were primarily in the thoracic (47.9%) and lumbar spine (47.3%). Crucially, no life-threatening (grade IV–V) complications occurred. The overall rate of grade I-III complications was low at 2.11%. Limitations: This systematic review is limited by its study-level nature, preventing detailed subgroup analyses regarding specific metastasis characteristics or the impact of complementary therapies. Conclusions: This systematic review suggests that RFA is a safe and effective treatment for pain control in patients with spinal metastases. It provides both rapid (within 24 h) and durable midterm (up to 24 weeks) analgesia. The favorable safety profile, with a low complication rate, supports RFA as a valuable complimentary option within the multidisciplinary palliative management of painful spinal secondary tumors. Future randomizedcontrolled studies may help to further define its role when associated with other treatments....
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