Tumours of the Lung – Small Diagnostic and Cytopathological Specimens

Datasets > Published Datasets > Thorax > Tumours of the Lung – Small Diagnostic and Cytopathological Specimens

Scope

The dataset has been developed for small diagnostic biopsy specimens and cytopathological specimens of lung cancer. It can also be used for benign tumours or other non-neoplastic specimens at the discretion of the cytopathologist/pathologist. In these cases, the ‘Other’ responses will be used in many situations and not all elements will be applicable. This flexibility of usage allows for a laboratory to use the one data set report for all lung biopsy specimens, whether or not the final report is one of cancer or a benign tumour or inflammatory process.

The dataset is constructed to include all information that is required for an ideal final report. However, it can be used for provisional reports that are issued before all testing is completed, so as to meet clinical and client needs prior to the issuing of a final all-encompassing report. Again, this flexibility allows for the one dataset to be used for all lung specimens, and unused sections can simply be deleted or preferably annotated as ‘results to follow’. It also includes a section for documentation of results from molecular analyses, when undertaken. While the authors recognise this dataset is long, this is necessitated by the multifaceted nature of specimen types and assessment in a rapidly evolving environment. However, a single document is favoured so that cytopathology and histopathology results are in the same place and can be compared.

The dataset is also applicable to fine needle aspiration biopsy or core needle biopsy or excision specimens of metastatic lesions from a primary lung cancer. Surgical resections of primary lung carcinomas are covered in the ICCR Lung cancer dataset. Mesothelioma is excluded from the scope of this dataset – refer to the ICCR Mesothelioma in the pleura and peritoneum dataset.

Details of synchronous primary tumours should be reported in separate datasets.