LYMPH NODE CYTOLOGY REPORTING BY “SYDNEY SYSTEM: A COMPREHENSIVE APPROACH TO LYMPH NODE FINE-NEEDLE ASPIRATION CYTOLOGY”

Objectives: The major goal of the current investigation was to categorize the lesions found in lymph nodes using the Sydney approach for reporting cytopathology of lymph nodes. Methods: This investigation was carried out retrospectively at Maharshi Vishwamitra autonomous state medical college in Ghazipur, between January 2022 and December 2023. Total 250 patients were examined; cytopathologically assessed, and categorized using the Sydney method. Whenever feasible, a link between histopathology and clinical findings was made. Results: Out of 250 cases, 04% (n=10) were inadequate/non-diagnostic (L1 category), 87% (n=218) were benign (L2 category), 02% (n=05) were atypical (L3 category), 2.8% (n=2.8%) were suspicious of malignancy (L4 category), and 04% (n=10) were malignant (L5 category). The sensitivity and specificity was 94.4% and 98.1%. The positive predictive value, negative predictive value, and diagnostic accuracy were 94.4%, 98.1%, and 97.1%, respectively. For L2 category, the probability of malignancy was 1.9 %, for L3 category 66.6% and for L4 and L5 was 100%. Conclusion: The suggested reporting system intends to provide consistent recommendations on management regimens, improve communication between cytopathologists and doctors, and standardize reporting.

Authors

Institutions

Publication Details

Journal
Asian Journal of Pharmaceutical and Clinical Research
Published
2025-05-07
DOI
https://doi.org/10.22159/ajpcr.2025v18i5.54339
Citations
1
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
1.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LYMPH NODE CYTOLOGY REPORTING BY “SYDNEY SYSTEM: A COMPREHENSIVE APPROACH TO LYMPH NODE FINE-NEEDLE ASPIRATION CYTOLOGY”

Abhishek Singh, Shivendra Dutt Shukla
1 citations
Asian Journal of Pharmaceutical and Clinical Research
Cervical Cancer and HPV Research
1.35
article

LYMPH NODE CYTOLOGY REPORTING BY “SYDNEY SYSTEM: A COMPREHENSIVE APPROACH TO LYMPH NODE FINE-NEEDLE ASPIRATION CYTOLOGY”

Abhishek Singh, Shivendra Dutt Shukla
article en
1 citations

Abstract

Objectives: The major goal of the current investigation was to categorize the lesions found in lymph nodes using the Sydney approach for reporting cytopathology of lymph nodes. Methods: This investigation was carried out retrospectively at Maharshi Vishwamitra autonomous state medical college in Ghazipur, between January 2022 and December 2023. Total 250 patients were examined; cytopathologically assessed, and categorized using the Sydney method. Whenever feasible, a link between histopathology and clinical findings was made. Results: Out of 250 cases, 04% (n=10) were inadequate/non-diagnostic (L1 category), 87% (n=218) were benign (L2 category), 02% (n=05) were atypical (L3 category), 2.8% (n=2.8%) were suspicious of malignancy (L4 category), and 04% (n=10) were malignant (L5 category). The sensitivity and specificity was 94.4% and 98.1%. The positive predictive value, negative predictive value, and diagnostic accuracy were 94.4%, 98.1%, and 97.1%, respectively. For L2 category, the probability of malignancy was 1.9 %, for L3 category 66.6% and for L4 and L5 was 100%. Conclusion: The suggested reporting system intends to provide consistent recommendations on management regimens, improve communication between cytopathologists and doctors, and standardize reporting.

Asian Journal of Pharmaceutical and Clinical Research
Maharani Laxmi Bai Medical College (IN)
Openalex Percentile: Top 20%
Cervical Cancer and HPV Research
1.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.