Fine Needle Aspiration Cytology of Salivary Gland Lesions and Its Diagnostic Pitfalls – A Tertiary Care Centre Experience From North East India

Roy, Asitava Deb; Deka, Muktanjalee; Dutta, Umesh Chandra

doi:10.14260/jemds/920

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Similar findings were reported by kumar et al, Ahmad et al and Fernandes et al [13][14][15] However our study was closely related with the findings of Ahmad et al 14 Pleomorphic adenoma was the commonest salivary gland lesions followed by sialadenitis which is similar to studies of Gandhi et al and Roy AD. 8,16 In Non-neoplastic lesions, submandibular gland was most commonly involved followed by parotid which were similar with findings by Gandhi SH and Singh Nanda. 8,9 In the present study, benign tumours were more common in the age group of 21-40 year, which is comparable with study done by Ahmad Set al…”

Section: Discussionsupporting

confidence: 82%

A study of fine needle aspiration cytology in salivary gland lesions in a rural population

Joshi

Jadhav

Baste³

et al. 2017

Int J Adv Med

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Background: Fine needle aspiration cytology (FNAC) of suspected salivary gland lesions has an established role in preoperative diagnosis and management of patients. However diverse morphological patterns and overlapping features make it a challenging job, to give a precise diagnosis at times.Methods: This was a prospective study done from October 2011 to October 2013. Fine needle aspiration cytology was performed in 64 patients with clinically significant salivary gland lesions.Results: Fine needle aspiration cytology categorized 38 (59.4%) of the salivary gland lesions as neoplastic and 26 (40.6 %) as non- neoplastic lesions. Amongst the neoplastic lesions, 86.9% were benign and 13.2% were malignant cases.Conclusions: FNAC of the salivary gland is simple, cheap, safe and reliable technique in the primary diagnosis of salivary gland lesions.

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Section: Discussionsupporting

confidence: 82%

A study of fine needle aspiration cytology in salivary gland lesions in a rural population

Joshi

Jadhav

Baste³

et al. 2017

Int J Adv Med

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“…It is not easy for cytologists to clearly distinguish the 2 entities especially with atypical cell arrangement or due to inexperience. [28][29][30] Fortunately, the ancillary techniques like immunohistochemistry (IHC) and molecular studies improve the distinction between ACC and PA. A p63+/p40immunophenotype that can help distinguish it from ACC and PA. 31 Strong nuclear staining for MYB is valuable for cytological diagnosis of ACC, 32,33 thus these patients may be amenable to Imatinib therapy. Fusion of MYB-NFIB and MYBL1-NFIB are altered in ACC, alteration of PLAG1 and HMGA2 in PA. 34 β-Catenin plays an important role in the pathogenesis of PAs and ACCs in salivary glands.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Cytological Misdiagnosis and Oversight of Adenoid Cystic Carcinoma of Salivary Gland

Huang

Li²,

Wang

et al. 2023

Cancer Control

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Objective In this article on adenoid cystic carcinoma (ACC) of salivary gland, we intend to summarize the causes of misdiagnosis and oversight of ACC hoping to improve cytological diagnostic accuracy, clinical management and patient treatment. Methods The study retrospectively reviewed 32 patients with ACC of salivary gland, registered at the Affiliated Hospital of Southwest Medical University from July 2014 to June 2021. These cases were diagnosed by FNA and surgical excision biopsy. All cytopathological results were retrospectively categorized according to Milan system for reporting salivary gland cytopathology (MSRSGC). The accuracy of FNA was verified by surgical excision biopsy. Results Of these 32 patients, 16 (50.0%) cases were male, and 16 (50.0%) were female. Their age ranged from 21 to 79 years, with an average age of 50.32 years. The highest incidence (15/32, 46.9%) of ACC was observed in patients between 41 and 50 years of age. 10 cases (31.3%) occurred in the parotid gland, 9 cases (28.1%) in the submandibular gland, 9 cases (28.1%) in the sublingual gland, 3 cases (9.4%) in the palate, and 1 case (3.1%) in the lip. Among the 32 cases of ACC, 23 cases (71.9%) were classified to VI, 4 cases (12.5%) to IVa, and 5 cases (15.6%) to II by MSRSGC. A comparison of the FNA results with biopsy showed that the accuracy of FNA in ACC of salivary gland is 71.9%. Being able to identify the cytomorphological features is the key factor for accurate diagnosis of ACC of the salivary gland. Conclusion Our results confirm that FNA is an important initial screening in the diagnosis of ACC of salivary gland. Increased study of the cytomorphology of ACC is beneficial for more accurate diagnosis of ACC, to reduce misdiagnosis and oversight.

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Artificial neural network model to distinguish pleomorphic adenoma from adenoid cystic carcinoma on fine needle aspiration cytology

2019

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Background To differentiate pleomorphic adenoma (PA) and adenoid cystic carcinoma (ACC) on fine needle aspiration cytology (FNAC) is often challenging to cytologists. Aims In the present study, we tried to make an artificial neural network (ANN) model from the FNAC smears to differentiate PA from ACC. Methods The detailed cytomorphological features were analysed on the FNAC of histopathology proven cases of PA (n = 35) and ACC (n = 33) and enumerated semi‐quantitatively by two independent observers. These data were used to make an ANN model to distinguish PA from ACC on FNAC material. We used neuro‐intelligence software to build the ANN model. The network architecture was 10‐2‐1. The heuristic search engine was applied to have this model. We used backpropagation neural network to teach ANN. At least 500 iterations were done to train the model. The efficacy of this ANN model was assessed with the help of the confusion matrix and receiver operating characteristic curve. Result The data were separated automatically by the software as a training set (n = 48), validation set (n = 10) and test set (n = 10). The ANN model was able to differentiate every case (10/10) of PA and ACC in the final test set. The area under the receiver operating characteristic curve was 1. Conclusion The currently built ANN model is competent to identify PA and ACC cases on FNAC. Additional parameters and new cases can be incorporated into this open‐ended ANN model to make it more robust.

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Fine Needle Aspiration Cytology of Salivary Gland Lesions and Its Diagnostic Pitfalls – A Tertiary Care Centre Experience From North East India

Cited by 3 publications

References 9 publications

A study of fine needle aspiration cytology in salivary gland lesions in a rural population

A study of fine needle aspiration cytology in salivary gland lesions in a rural population

Analysis of Cytological Misdiagnosis and Oversight of Adenoid Cystic Carcinoma of Salivary Gland

Artificial neural network model to distinguish pleomorphic adenoma from adenoid cystic carcinoma on fine needle aspiration cytology

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