Lung Cancer Diagnosis by Fine Needle Aspiration Is Associated With Reduction in Resection of Nonmalignant Lung Nodules

BackgroundLung cancer is still the main cause of cancer deaths. The high lung cancer mortality rate is caused by a diagnosis factor or therapy selection. The cell block cytology technique using fine needle aspiration (FNA) samples can provide immunocytochemical material that plays an important role in the differential diagnosis of lung cancer subtypes and in determining immunotherapy administration. This study aimed to determine the sensitivity and specificity of transthoracic FNA (TTFNA) cell block cytology in comparison with bronchial washing smears and TTFNA smears in diagnosing lung cancer. MethodsThis was a cross-sectional diagnostic study involving 26 subjects. All subjects had undergone bronchial washing and CT scan-guided fine needle aspiration followed by cell block preparation. Both direct FNA smears and cell blocks are useful in the diagnostic work-up of patients. Comparative statistical analysis of TTFNA cell block versus bronchial washing smear and TTFNA smear cytology was carried out using the McNemar test. ResultsLung cancer was found in 15 patients (57.7%) using the TTFNA cell block technique. The sensitivity and specificity of the TTFNA cell block technique were 85.7% and 75%, respectively. There was no difference in the positivity value between TTFNA cell block technique of bronchial wash smear technique, and TTFNA smear on lung cancer diagnosis (p>0.05). ConclusionsTransthoracic fine-needle aspiration in combination with the cell block technique has good sensitivity and specificity. The TTFNA can be used for immunocytochemical examinations in lung cancer diagnosis and therapy. This approach is valuable for providing individualized treatment and prognostic evaluations.

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The value of cell block based on fine needle aspiration for lung cancer diagnosis

Journal of Thoracic Disease ◽

10.21037/jtd.2017.07.91 ◽

2017 ◽

Vol 9 (8) ◽

pp. 2375-2382 ◽

Cited By ~ 10

Author(s):

Zhengwei Dong ◽

Hui Li ◽

Jun Zhou ◽

Wei Zhang ◽

Chunyan Wu

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Fine Needle Aspiration ◽

Needle Aspiration ◽

Cell Block ◽

Fine Needle ◽

Lung Cancer Diagnosis ◽

Block Based

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Transthoracic fine-needle aspiration diagnosis of solid, subsolid, and partially calcified lung nodules: A retrospective study from a single academic center

CytoJournal ◽

10.4103/cytojournal.cytojournal_43_18 ◽

2019 ◽

Vol 16 ◽

pp. 16 ◽

Cited By ~ 1

Author(s):

Yangying Zhou ◽

Gary Gong ◽

Haiyan Wang ◽

Zahra Alikhassy Habibabady ◽

Peggy Lang ◽

...

Keyword(s):

Lung Cancer ◽

Fine Needle Aspiration ◽

Large Scale ◽

Early Stage ◽

Lung Nodule ◽

Needle Aspiration ◽

Lung Nodules ◽

Computer Search ◽

Radiographic Findings ◽

Fine Needle

Background: The large-scale National Lung Cancer Screening Trial demonstrated an increased detection of early-stage lung cancers using low-dose computed tomography scan in the screening population. It also demonstrated a 20% reduction of lung cancer-related deaths in these patients. Aims: Although both solid and subsolid lung nodules are evaluated in studies, subsolid and partially calcified lung nodules are often overlooked. Materials and Methods: We reviewed transthoracic fine-needle aspiration (FNA) cases from lung nodule patients in our clinics and correlated cytological diagnoses with radiologic characteristics of lesions. A computer search of the pathology archive was performed over a period of 12 months for transthoracic FNAs, including both CT- and ultrasound-guided biopsies. Results: A total of 111 lung nodule cases were identified. Lesions were divided into three categories: solid, subsolid, and partially calcified nodules according to radiographic findings. Of 111 cases, the average sizes of the solid (84 cases), subsolid (22 cases), and calcified (5 cases) lesions were 1.952 ± 2.225, 1.333 ± 1.827, and 1.152 ± 1.984 cm, respectively. The cytological diagnoses of three groups were compared. A diagnosis of malignancy was made in 64.28% (54 cases) in solid, 22.72% (5 cases) in subsolid, and 20% (1 case) in partially calcified nodules. Among benign lesions, eight granulomatous inflammations were identified, including one case of solid, five cases of subsolid, and two cases of calcified nodules. Conclusions: Our study indicates that solid nodules have the highest risk of malignancy. Furthermore, the cytological evaluation of subsolid and partially calcified nodules is crucial for the accurate diagnosis and appropriate clinical management of lung nodule patients.

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽

10.3390/math9131457 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1457

Author(s):

Muazzam Maqsood ◽

Sadaf Yasmin ◽

Irfan Mehmood ◽

Maryam Bukhari ◽

Mucheol Kim

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Search Space ◽

Lung Nodules ◽

Suggested Model ◽

Lung Cancer Diagnosis ◽

Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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