Diagnosis and Detection of Orally Potential Malignant Lesions – A Systematic Review

2021 ◽  
Vol 11 (1) ◽  
pp. 73-82
Author(s):  
Nitin Verma ◽  
Neha Mann ◽  
Amreen Kaur ◽  
Supreet Gill ◽  
Danish Khurana ◽  
...  
2021 ◽  
Author(s):  
Chiel F. Ebbelaar ◽  
Anne M. L. Jansen ◽  
Lourens T. Bloem ◽  
Willeke A. M. Blokx

AbstractCutaneous intermediate melanocytic neoplasms with ambiguous histopathological features are diagnostically challenging. Ancillary cytogenetic techniques to detect genome-wide copy number variations (CNVs) might provide a valuable tool to allow accurate classification as benign (nevus) or malignant (melanoma). However, the CNV cut-off value to distinguish intermediate lesions from melanoma is not well defined. We performed a systematic review and individual patient data meta-analysis to evaluate the use of CNVs to classify intermediate melanocytic lesions. A total of 31 studies and 431 individual lesions were included. The CNV number in intermediate lesions (median 1, interquartile range [IQR] 0–2) was significantly higher (p<0.001) compared to that in benign lesions (median 0, IQR 0–1) and lower (p<0.001) compared to that in malignant lesions (median 6, IQR 4–11). The CNV number displayed excellent ability to differentiate between intermediate and malignant lesions (0.90, 95% CI 0.86–0.94, p<0.001). Two CNV cut-off points demonstrated a sensitivity and specificity higher than 80%. A cut-off of ≥3 CNVs corresponded to 85% sensitivity and 84% specificity, and a cut-off of ≥4 CNVs corresponded to 81% sensitivity and 91% specificity, respectively. This individual patient data meta-analysis provides a comprehensive overview of CNVs in cutaneous intermediate melanocytic lesions, based on the largest pooled cohort of ambiguous melanocytic neoplasms to date. Our meta-analysis suggests that a cut-off of ≥3 CNVs might represent the optimal trade-off between sensitivity and specificity in clinical practice to differentiate intermediate lesions from melanoma.


2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
David Tovmassian ◽  
Muzib Abdul Razak ◽  
Kevin London

Background. Malignant peripheral nerve sheath tumours (MPNSTs) are difficult to diagnose and treat and contribute to significant morbidity and mortality for patients with Neurofibromatosis-1 (NF-1). FDG-PET/CT is being increasingly used as an imaging modality to discriminate between benign and malignant plexiform neurofibromas. Objectives. To assess the value of FDG-PET/CT in differentiating between benign and malignant peripheral nerve lesions for patients with Neurofibromatosis-1. Methods. A systematic review of the literature was performed prior to application of stringent selection criteria. Ultimately 13 articles with 796 tumours were deemed eligible for inclusion into the review. Results. There was a significant difference between mean SUVmax of benign and malignant lesions (1.93 versus 7.48, resp.). Sensitivity ranged from 89 to 100% and specificity from 72 to 94%. ROC analysis was performed to maximise sensitivity and specificity of SUVmax cut-off; however no clear value was identified (range 3.1–6.1). Significant overlap was found between the SUVmax of benign and malignant lesions making differentiation of lesions difficult. Many of the studies suffered from having a small cohort and from not providing histological data on all lesions which underwent FDG-PET/CT. Conclusion. This systematic review is able to demonstrate that FDG-PET/CT is a useful noninvasive test for discriminating between benign and malignant lesions but has limitations and requires further prospective trials.


2010 ◽  
Vol 30 (5) ◽  
pp. 665-672 ◽  
Author(s):  
Iuri Usêda Santana ◽  
Alline do Nascimento Gomes ◽  
Leomar D’Cirqueira Lyrio ◽  
Maria Fernanda Rios Grassi ◽  
Mittermayer Barreto Santiago

Surgery ◽  
2015 ◽  
Vol 157 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Arianeb Mehrabi ◽  
Mohammadreza Hafezi ◽  
Jalal Arvin ◽  
Majid Esmaeilzadeh ◽  
Camelia Garoussi ◽  
...  

Author(s):  
Antonio Romano ◽  
Dario Di Stasio ◽  
Enrica Gentile ◽  
Massimo Petruzzi ◽  
Rosario Serpico ◽  
...  

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