Background: Optical coherence tomography (OCT) is an optical imaging technique that may be useful in diagnosis of non-melanoma skin cancer (NMSC).
Objectives: To describe OCT features in NMSC such as actinic keratosis (AK) and basal cell carcinoma (BCC) and in benign lesions and to assess the diagnostic accuracy of OCT in differentiating NMSC from benign lesions and normal skin.
Methods And Materials: OCT and polarization-sensitive (PS) OCT from 104 patients were studied. Observer-blinded evaluation of OCT images from 64 BCCs, 1 baso-squamous carcinoma, 39 AKs, two malignant melanomas, nine benign lesions, and 105 OCT images from perilesional skin was performed; 50 OCT images of NMSC and 50 PS-OCT images of normal skin were evaluated twice.
Results: Sensitivity was 79% to 94% and specificity 85% to 96% in differentiating normal skin from lesions. Important features were absence of well-defined layering in OCT and PS-OCT images and dark lobules in BCC. Discrimination of AK from BCC had an error rate of 50% to 52%.
Conclusion: OCT features in NMSC are identified, but AK and BCC cannot be differentiated. OCT diagnosis is less accurate than clinical diagnosis, but high accuracy in distinguishing lesions from normal skin, crucial for delineating tumor borders, was obtained.
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http://dx.doi.org/10.1111/j.1524-4725.2009.01164.x | DOI Listing |
World J Surg Oncol
January 2025
Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.
BMC Gastroenterol
January 2025
Faculty of Medicine, University of Khartoum, Khartoum, 11111, Sudan.
Background & Objectives: Differentiation of histologic subtypes of appendiceal mucoceles may prove to be difficult on computed tomography (CT). The main objective of this study was to identify the CT features of mucocele of the appendix and correlate the imaging findings with histopathology in inflammatory, benign, and malignant neoplastic lesions, and whether these entities can be accurately differentiated on CT imaging.
Materials And Methods: CT scans of 31 patients with diagnosis of appendiceal mucocele were retrospectively reviewed and compared with histopathology.
Ann Surg Oncol
January 2025
Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Background: Atypical ductal hyperplasia (ADH) is a benign proliferative breast lesion. Surgical excision of ADH is often recommended to rule out underlying malignant disease.
Objective: The aim of this study was to evaluate the trends in ADH upgrade rates over time and identify the impact of magnetic resonance imaging (MRI) use on upgrade rates.
Dermatologie (Heidelb)
January 2025
MVZ Dermatohistopathologie Heidelberg, Mönchhofstr. 52, 69120, Heidelberg, Deutschland.
Background: The update of the World Health Organisation (WHO) classification of skin tumours has led to new aspects in the classification of melanocytic tumours.
Objectives: Presentation of the classification of melanocytic tumours in light of current clinical, histological and genetic data.
Materials And Methods: Review of the classification of melanocytic neoplasms in the fifth edition of the WHO classification of skin tumours, taking into account current disease concepts.
J Biophotonics
January 2025
Clinical Research Center for Respiratory Disease (Tumor) in Anhui Province, Bengbu, Anhui Province, China.
In this study, a non-invasive device based on ultraviolet differential absorption spectroscopy (UV-DOAS) technology for detecting fractional exhaled nitric oxide (FeNO)was developed and clinically validated in patients with various lung diseases. The diagnostic potential of FeNO was explored by analysing subgroups of patients with lung cancer, nodules, and other disease. The results showed that FeNO concentrations were significantly higher in patients with malignant tumours than in healthy controls (p < 0.
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