Journal Thoracic Oncology

Abstracts Journal of Thoracic Oncology • Volume 12 Issue S1 January 2017
factors should be taken into account when interpreting FDG-PET-CT SUV
values in clinical practice. The correlation of FDG-PET-CT SUV values with
inflamed tumour phenotypes, and the possible predictive value of SUV for
response to immune therapies, should be further investigated.
Keywords: PET-CT, Adenocarcinoma, proliferation, inflammation
POSTER SESSION 1 - P1.03: RADIOLOGY/STAGING/SCREENING
RADIOLOGY –
MONDAY, DECEMBER 5, 2016
P1.03-028 WOLF IN SHEEP’S CLOTHING - PRIMARY LUNG CANCER
MIMICKING BENIGN DISEASES
Annemie Snoeckx 1 , Damien Desbuquoit 1 , Amélie Dendooven 2 , Maarten
Spinhoven 1 , Birgitta Hiddinga 3 , Laurens Carp 4 , Paul Van Schil 5 , Paul Parizel 1 ,
Jan Van Meerbeeck 1
1 Department of Radiology, Antwerp University Hospital and University of Antwerp,
Edegem/Belgium, 2 Department of Pathology, Antwerp University Hospital and
University of Antwerp, Edegem/Belgium, 3 Department of Thoracic Oncology,
Antwerp University Hospital and University of Antwerp, Edegem/Belgium,
4 Department of Nuclear Medicine, Antwerp University Hospital and University
of Antwerp, Edegem/Belgium, 5 Department of Thoracic and Vascular Surgery,
Antwerp University Hospital and University of Antwerp, Edegem/Belgium
Background: Lung cancer is the biggest cancer killer and typically presents
as mass or nodule, round or oval in shape. Recognition and diagnosis of these
typical cases is often straightforward, whereas diagnosis of uncommon
manifestations of primary lung cancer certainly is far more challenging. The
aim of this pictorial essay is to illustrate the Computed Tomography (CT) and
histopathology findings of uncommon manifestations of primary lung cancer
with focus on these entities that mimic benign diseases. Methods: Cases
presented were collected during the Multidisciplinary Thoracic Oncology
Tumor Board between January 2014 and May 2016 and have histopathologic
proof. Results: Lung cancer can mimic a variety of benign diseases,
including infection, granulomatous disease, lung abscess, postinfectious
scarring, mediastinal mass, emphysema, atelectasis and pleural disease.
Previous history, clinical and biochemical parameters are certainly helpful
and necessary in the assessment of these cases, but often aspecific and
inconclusive. Whereas 18FDG-PET is the cornerstone in diagnosis and
staging of lung cancer, it’s role in these uncommon manifestations is less
straightforward since benign diseases, such as granulomatous and infectious
diseases may also present with increased FDG-uptake. Chest CT is the
imaging modality of choice and plays a central role in these cases. ‘Irregular
air bronchogram sign’ in pneumonia-like lung cancer, ‘drowned lung sign’ in
obstructive atelectasis and cortical bone erosion in lung cancer mimicking
pleural disease are important signs that point to a malignant etiology. The
stippled and eccentric morphology of calcifications in apical lesions aids
in differentiating these lesions from postinfectious scarring. Mucinous
tumours can mimic a pulmonary abscess and small cell lung cancer can
typically present as mediastinal mass without parenchymal abnormalities.
Lung cancer presenting with a miliary pattern or cavitating nodules can
mimic granulomatous disease. Lung cancer presenting with cystic airspaces
and ‘emphysema-like’ morphology is an uncommon entity in which early
recognition is crucial since these tumors have an aggressive nature. Key
imaging findings and tips and tricks for recognizing these uncommon faces
of primary lung cancer will be discussed and illustrated. Conclusion: Primary
lung cancer can mimic a wide variety of benign entities. Knowledge of these
uncommon and atypical manifestations is crucial to avoid delay in diagnosis
and treatment. A multidisciplinary approach in these cases is mandatory.
Keywords: lung cancer, computed tomography, Mimickers, nonnenplastic
pulmonary disease
POSTER SESSION 1 - P1.03: RADIOLOGY/STAGING/SCREENING
RADIOLOGY –
MONDAY, DECEMBER 5, 2016
P1.03-029 A USEFUL ALGORITHMATIC MODEL IN PREDICTING
THE LIKELIHOOD OF LUNG CANCER IN SOLITARY PULMONARY
NODULES
Lin Gen, Zhuang Wu, Rao Ying, Zhu Shou, Zheng Feng, Chen Hui, Li Cheng,
Chen Ping, Xu Wei, Wu Biao, Xu Wu, Huang Jian, Zhang Jin, Huang Cheng
Fujian Provicial Cancer Hospital, the Affiliated Hospital of Fujian Medical
University, Fuzhou/China
Background: The aim of this study was to establish a mathematic model to
predict the likelihood of lung cancer in surgically resected solitary pulmonary
nodules (SPNs) and investigate the value of multidisciplinary treatment
(MDT) consultation in diagnosis of SPNs. Methods: From January 2011
to June 2016, 666 patients with a clear pathological diagnosis of SPN by
surgical resection in Fujian Provicial Cancer Hospital were involved. Their
clinicopathologic data were collected and retrospectively analyzed. All
patients were divided into testing and validating cohorts, testing cohort
consisted of patients from January 2011 to June 2015, whose data were used
to create a mathematical model via multivariate logistic regression analysis.
Patients from July 2015 to June 2016 were included in validating cohort,
whose data were used to verify the accuracy of the prediction model. The
positive rate of malignancy between cases discussed at MDT meeting and
evaluated by surgeon were compared. Results: The number of testing and
validating cohorts was 446 and 220, respectively. In testing cohort, there
were 8 case (1.8%) diagnosed as atypical adenohyperplasia (AAH) and 313
cases (70.2%) as malignant SPNs, mainly invasive adenocarcinoma (IA, 234
cases/ 52.5%), small cell lung cancer (SCLC, 28 cases/6.3%), minimally invasive
adenocarcinoma (MIA, 22 cases/4.9%) and adenocarcinoma in situ(AIS,10
cases/2.2%). Other were benign SPNs (125 cases, 28%), mainly including
inflammation or fibrosis(95 cases, 21.3%), hamartoma (17 cases, 3.8%) and
inflammatory pseudotumor (11 cases, 2.4%).Univariate analysis showed
that there were significant differences between benign and malignant SPNs
regarding age, sex, nodule type, maximum nodule diameter, CT value, nodule
shape, spiculation, lobulation, pleural retraction sign, cavitation, bronchiole
truncation and vascular convergence (P