Faculty Bibliography

Sarcomatoid carcinoma is rarely found in pleural or pericardial fluid, with very few cases published to date. Here, we describe a 59-year-old female who presented with cough persisting for 5 months. Chest CT scan revealed a 6.0 cm cavitary mass in the left lung base with bulky mediastinal and hilar lymphadenopathy. An additional 1.2 cm right adrenal mass was seen and was suspicious for metastatic disease. The patient developed dyspnea, tachycardia, pleuritic chest pain and generalized weakness and was admitted to the hospital. She was found to have pleural and pericardial effusions, which were drained and sent to cytology. The fluid revealed enlarged highly pleomorphic malignant cells, some displaying multinucleation with irregular nuclear borders, coarse chromatin and prominent nucleoli. Tumor cells were positive for CK7 and Vimentin and negative for MOC-31, Ber-EP4, B72.3, Sox10, Melan-A, TTF-1, Napsin-A and CK20. A concurrent surgical biopsy of the tumor mass displayed immunopositivity for AE1/AE3 and CAM5.2. The tumor was negative for p40, TTF-1, calretinin, D2-40 and STAT6. A diagnosis of sarcomatoid carcinoma with giant cells and spindle cells was rendered. Sarcomatoid carcinomas of the lung are very uncommon consisting of 1% of non-small-cell lung carcinomas and are even more unusual in cytology specimens. Despite its rarity, it is important to keep this entity in mind in the differential diagnosis of a fluid specimen with bizarre nuclear atypia and the above staining pattern.

We assessed a priori aggressive features using quantitative diffusion-weighted imaging metrics to preclude an active surveillance management approach in patients with papillary thyroid cancer (PTC) with tumor size 1-2 cm. This prospective study enrolled 24 patients with PTC who underwent pretreatment multi-b-value diffusion-weighted imaging on a GE 3 T magnetic resonance imaging scanner. The apparent diffusion coefficient (ADC) metric was calculated from monoexponential model, and the perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), and diffusion kurtosis coefficient (K) metrics were estimated using the non-Gaussian intravoxel incoherent motion model. Neck ultrasonography examination data were used to calculate tumor size. The receiver operating characteristic curve assessed the discriminative specificity, sensitivity, and accuracy between PTCs with and without features of tumor aggressiveness. Multivariate logistic regression analysis was performed on metrics using a leave-1-out cross-validation method. Tumor aggressiveness was defined by surgical histopathology. Tumors with aggressive features had significantly lower ADC and D values than tumors without tumor-aggressive features (P < .05). The absolute relative change was 46% in K metric value between the 2 tumor types. In total, 14 patients were in the critical size range (1-2 cm) measured by ultrasonography, and the ADC and D were significantly different and able to differentiate between the 2 tumor types (P < .05). ADC and D can distinguish tumors with aggressive histological features to preclude an active surveillance management approach in patients with PTC with tumors measuring 1-2 cm.

Fine needle aspiration cytology (FNAC) of mediastinal masses allows for rapid on-site evaluation and the triaging of material for ancillary studies. However, surgical pathology is often considered to be the gold standard for diagnosis. This study examines the sensitivity and specificity of FNAC compared to a concurrent or subsequent surgical pathology specimen in 77 mediastinal lesions. The overall sensitivity for mediastinal mass FNAC was 78% and the overall specificity was 98%. For individual categories the sensitivity and specificity of FNAC was respectively as follows: inflammatory/infectious (33%, 99%), metastatic carcinoma (93%, 100%), lymphoma (84%, 97%), cysts (25%, 100%), soft tissue tumors (100%, 100%), paraganglioma (50%, 100%), germ cell tumor (100%, 99%), thymoma (87%, 94%), thymic carcinoma (60%, 100%), benign thymus (0%, 100%), and indeterminate (100%, 90%). For different locations within the mediastinum the sensitivity and specificity of FNAC was respectively as follows: anterosuperior mediastinum (80%, 98%), posterior mediastinum (33%, 95%), middle mediastinum (100%, 100%), and mediastinum, NOS (79%, 99%). Thus, mediastinal FNAC is fairly sensitive, very specific, and is a valuable technique in the diagnosis of mediastinal masses.

Objectives/UNASSIGNED:To evaluate whether non-small cell lung carcinoma (NSCLC) cytology specimens are reliable for programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) testing. Methods/UNASSIGNED:Fifty-two cell blocks (CBs) with corresponding surgical pathology PD-L1 IHC testing were stained with a Dako PD-L1 pharmDX antibody (clone-22C3). Tumor cellularity was recorded as <100 or ≥100 cells. PD-L1 IHC was scored by percentage of tumor cells staining (<1%, ≥1%-49%, ≥50%) and compared between matched cases. Results/UNASSIGNED:Substantial agreement (κ = 0.63; 95% CI, 0.53-0.73) was reached between matched CB and surgical cases in CBs with ≥100 tumor cells compared to CBs with <100 tumor cells (slight agreement, κ = 0.19; 95% CI, 0.04-0.35). Overall, there was 67% agreement among paired cases (35/52 cases, κ = 0.51; 95% CI, 0.42-0.60). Conclusions/UNASSIGNED:CBs can be utilized for PD-L1 IHC testing, as illustrated by the 67% agreement between CB and surgical cases in our study. Disagreement is attributable to intratumoral heterogeneity and CB cellularity.

Since the 2015 WHO classification was introduced into clinical practice, the importance of immunohistochemistry (IHC) has figured prominently in lung cancer diagnosis. In addition to distinction of small versus non-small cell carcinoma (NSCC), patients' treatment of choice is directly linked to histological subtypes of NSCC, which pertains to IHC results, particularly for poorly-differentiated tumors. The use of IHC has improved diagnostic accuracy in the lung carcinoma classification, but the interpretation remains challenging in some instances. Also, pathologists must be aware of many interpretation pitfalls, and the use of IHC should be efficient to spare the tissue for molecular testing. The IASLC Pathology Committee received questions on practical application and interpretation of IHC in lung cancer diagnosis. After discussions in several IASLC Pathology Committee meetings, the issues and caveats were summarized as eleven key questions, which cover common and important diagnostic situations in a daily clinical practice with some relevant challenging queries. The questions included best IHC markers for distinguishing NSCLC subtypes, differences in TTF1 clones, utility of IHC in diagnosing uncommon subtypes of lung cancer and distinguishing primary from metastatic tumors." This article provides answers and explanations for the key questions about the use of IHC in lung carcinoma diagnosis representing viewpoints of experts in thoracic pathology that should assist the community in the appropriate use of IHC in diagnostic pathology.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .