Organising Pneumonia and Lung Cancer—Case Report and Review of the Literature

Radzikowska, Elżbieta; Nowicka, Urszula; Wiatr, Elżbieta; Jakubowska, Lila; Langfort, Renata; Chabowski, Mariusz; Roszkowski, K.

doi:10.5603/arm.27961

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…OP commonly lacks a direct known causative agent, and is thus often categorized as cryptogenic OP [26]. While lung cancer-associated OP has been documented in the literature, its occurrence remains relatively uncommon [27,28]. The etiology of cancer-related OP is not definitively established, with uncertainties persisting regarding whether it represents a secondary manifestation of malignant disease, a consequence of chemo/radiation therapy, or a concomitant presentation.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

Sano,

Okoshi,

Tachibana

et al. 2024

Cancers

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Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Results: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set. Conclusion: The machine-learning model developed has the potential to distinguish between benign and malignant conditions in TBLB samples excluding the presence or absence of tumor cells, thereby improving diagnostic accuracy and reducing the burden of repeated sampling procedures for patients.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

Sano,

Okoshi,

Tachibana

et al. 2024

Cancers

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Organizing pneumonia secondary to lung cancer of unknown primary site

Mao

Zhang

Hou

et al. 2019

Respiratory Medicine Case Reports

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Background Secondary organizing pneumonia (OP) is associated with other pathological conditions, such as infections, drugs, cancers and radiotherapy. Lung cancer-associated secondary OP has rarely been reported. Case review In this study, we reported on a case of secondary OP caused by lung cancer. The patient was initially diagnosed with community-acquired pneumonia and then cryptogenic organizing pneumonia by CT scan-guided and transbronchial lung biopsy. Poor response to anti-infection or corticosteroid therapy prompted us to search for underlying disease. A TBNA biopsy of the 4R mediastinal lymph node revealed the diagnosis of lung cancer. Conclusion OP secondary to lung cancer of unknown primary site are rare. When OP patients have lymphadenopathy or poor response to glucocorticoid, a more differential diagnosis should be considered, especially for avoiding the misdiagnosis of a malignancy.

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Breast cancer and organising pneumonia: the importance of correlating the pathological findings with the clinical and radiological picture

Griffiths¹,

Doran

Fullerton

2018

BMJ Case Reports

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This case describes a female patient with a history of breast cancer who presented with a persistent cough and weight loss after a 17-year disease-free period. Radiologically there were new bilateral intrapulmonary nodules and areas of consolidation with a broad differential diagnosis. Brushings gained via bronchoscopy were suggestive of malignancy, but subsequent video-assisted thoracoscopic surgery wedge resection demonstrated organising pneumonia (OP) with no evidence of malignancy. Diagnostic uncertainty remained after this, and after a period of observation, there was evidence of disease progression. Further tissue was obtained for cytological and histological assessment which provided conclusive evidence of metastatic breast cancer. In this case, OP was secondary to proximal bronchial obstruction due to metastatic infiltration, of which there are no reported similar cases in the current literature. We discuss the importance of differentiating secondary OP from cryptogenic OP.

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Organising Pneumonia and Lung Cancer—Case Report and Review of the Literature

Cited by 6 publications

References 11 publications

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

Organizing pneumonia secondary to lung cancer of unknown primary site

Breast cancer and organising pneumonia: the importance of correlating the pathological findings with the clinical and radiological picture

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