Background: This prospective study assesses the use of rapid remote online cytological evaluation for diagnosing endoscopical achieved biopsies. It focuses on its effectiveness in identifying benign and malignant conditions using digital image processing. Methods: The study was conducted between April 2021 and September 2022 and involved analyses of 314 Rapid Remote Online Cytological Evaluations in total (154 imprint cytologies, 143 fine needle aspirations and 17 brush cytologies) performed on 239 patients at the LungenClinic Grosshansdorf. During on-site evaluation via telecytology, the time requirement was recorded and the findings were compared with the cyto-/histological and final diagnoses. Results: By means of rapid remote online evaluation, findings of 86 cytological benign, 190 malignant and 38 unclear diagnoses were recorded (Ø assessment time, 100 s; range, 11–370 s). In 27 of the 37 specimens with unclear diagnoses, the final findings were malignant tumours and only 6 were benign changes. The diagnosis of another 4 of these 37 findings remained unclear. Excluding these 37 specimens, rapid remote online evaluation achieved a sensitivity of 90.5% with a specificity of 98.5% and a correct classification rate of 92.4% with regard to the final diagnosis of all cases. As expected, an increase in the sensitivity rate for the cytological detection of malignant tumours (76.1% vs. 92.5%) was found especially in fine-needle aspirations. Conclusions: Rapid remote online analysis allows the fast quantitative and qualitative evaluation of clinically obtained cytological specimens. With a correct classification rate of more than 93%, sampling deficiencies can be corrected promptly and diagnostic and therapeutic approaches can be derived.

Objectives The majority of lung cancer cases are of advanced stage and diagnosis is usually made using minimally invasive small biopsies and cytological specimens. The WHO’2015 classification recommends limiting immunocytochemistry(ICC) to lung cancer typing and molecular testing drives for personalized therapies. An algorithm using Bayes' theorem could be useful for defining antibody profiles. This study aims to assess the impact of using different antibody profiles for cytological samples on the accuracy of lung cancer typing with a large scale Bayesian analysis. Methods Between 2011-2016 a total of 3,419 consecutive smears and/or cytospins were diagnosed. There were 1,960 primary lung cancer (972 adenocarcinomas (ADC), 256 squamous carcinomas(SQC), 268 neuroendocrine tumors[(NET);166 SCLC; 85 large cell neuroendocrine carcinoma(LCNEC),7 atypical carcinoid(AC),10 typical carcinoid(TC)], and 464 tumors without any ADC, SQC or neuroendocrine growth features(NSCC-NOS). Before and after the use of ICC with single/combination of antibodies, the a priori and a posteriori probabilities for different lung cancer types were calculated. Results TTF-1 or CK7 alone improves the a posteriori probabilities of correct cytological typing of ADC to 86.5% and 95.8% respectively. In SQC, using p40(∆Np63) or CK5/6, CK5/14 led to comparable results (78.3% and 90.3%). With synaptophysin or CD56 alone a posteriori probabilities for the correct recognition of NET of 87.5 and 90.3% could be achieved. Conclusions Based on morphological and clinical data, the use of two antibodies appears to be sufficient for the reliable detection of the different lung cancer types. This applies to both clinical-cytological and histological-only validated final diagnoses.

Intimal sarcoma of blood vessels is a rare, aggressive tumor originating from vascular endothelial cells. This report presents a 22-year-old male diagnosed with an intimal sarcoma of the lower pulmonary vein, detailing diagnosis, treatment, and prognosis information. Additionally, this report explores the application application of Endobronchial Ultrasound-Guided Fine-Needle Aspiration (EBUS-FNA) alongside with Rapid Remote Online Evaluation (ROLE) for identifying a mass-like lesion in the pulmonary vein. We emphasize the value of this technique for supporting ancillary studies like immunohistochemistry. The importance of accurate diagnostic methods and personalized treatment management to improve prognosis in rare cancers is highlighted by this thorough analysis.

Lung cancer is a highly heterogeneous disease that presents significant challenges in accurate diagnosis and classification due to its diverse histological and molecular characteristics. Traditional diagnostic methods, while valuable, are often limited by invasiveness, subjectivity, and an inability to fully capture tumor complexity. Recent advancements in artificial intelligence (AI), machine learning, and radiomics have transformed the field, offering enhanced precision, efficiency, and objectivity in lung cancer classification. These technologies enable detailed analyses of imaging data, histopathological findings, and molecular profiles, facilitating improved subtype identification, outcome prediction, and personalized treatment strategies. Cytopathology remains a cornerstone of lung cancer diagnostics, particularly for small biopsies and cytological samples, which are often the only materials available in advanced stages. The integration of AI-driven methods into cytopathology and radiomics workflows has shown substantial potential to overcome the limitations of traditional approaches, reduce interobserver variability, and accelerate the diagnostic process. This review underscores the transformative role of AI and radiomics in lung cancer management, highlighting their synergy in advancing precision oncology. As ongoing research continues to refine these methodologies, the future of lung cancer care is poised for significant advancements, offering improved diagnostic accuracy, personalized therapies, and better patient outcomes.

Background: Major diagnostic and therapeutic changes led to the implementation of the ‘lung cancer diagnosis in small biopsies and cytology specimens’ concept in the WHO Classification of Tumours of the Lung, Pleura, Thymus and Heart in 2015. Touch imprints are an established technique in cytology that provides a rapid and minimally invasive method for morphological diagnoses of clinical specimens, accurate subtyping, and molecular characterizations of malignancies. The extended diagnostic requirements from the increasingly limited material provided by minimally invasive biopsy techniques pose major challenges for pathology. Recognizing the relevant features and potential pitfalls is essential for cytologists to avoid misinterpretation. Materials and Methods: A retrospective analysis of endoscopic and surgical biopsy diagnostics was performed on 717 patients (303 women and 414 men; average age of 66.9 years) with clinically suspicious tumor findings at the LungenClinic Grosshansdorf in 2020. A total of 1363 cytological samples were obtained pre-therapeutically from 986 distinct biopsies covering 330 surgically and 656 endoscopically examined pulmonary, mediastinal, and bronchial regions. These samples were then compared with the histological diagnoses that were critical for determining the final therapy. Results: Out of a total of 656 endoscopically examined cases, 322 were classified as malignant, 308 as benign, and 26 as undetermined. While touch imprints and histological analysis separately achieved specificity values of 95.4% and 98.8%, both methods showed sensitivity values of 82.1% and 86.5%, respectively. In contrast, combining the two methods improved the sensitivity by 8 percentage points to 94.6%. Out of 330 cases of surgically examined samples, 137 were malignant, 190 were benign, and 3 were undetermined. The specificity of the morphological examinations for these samples was comparably high at 97.9% and 100%, respectively. In this surgical setting, touch imprints alone achieved a sensitivity of 75.9% (n = 104/137 cases), with a specificity of 97.9% (n = 186/190 cases). The outcome of the histological approach alone and in combination with touch imprints reached a sensitivity of 96.4% (n = 132/137 cases). Conclusions: Cytology and histology achieved comparably high sensitivity and specificity values on small biopsies. Under optimal conditions for morphological analysis in a surgical setting, the sensitivity of cytology for detecting malignant tumors was only 6 percentage points lower compared with the clinical endoscopic setting. A combined approach of cytologic–histologic evaluation for endoscopically examined specimens significantly increased the sensitivity by approximately 8% compared with the surgical setting (p < 0.003).