How trustworthy is a diagnosis in head and neck surgical pathology? A consideration of diagnostic discrepancies (errors)

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.

Predictors of recurrence and survival for head and neck mucoepidermoid carcinoma

OBJECTIVE

To describe the epidemiology and determine the clinicopathologic predictors of recurrence and survival in patients with head and neck mucoepidermoid carcinoma (MEC).

STUDY DESIGN

Case series with chart review.

SETTING

Tertiary care hospital.

SUBJECTS AND METHODS

The medical records of 101 patients who underwent surgical treatment at the University of Michigan for head and neck MEC between 1985 and 2010 were reviewed. Main outcome measures were disease-free survival (DFS), disease-specific survival (DSS), and overall survival. Clinicopathologic parameters evaluated were age, sex, subsite, histological grade, adjuvant therapy, T stage, nodal status with/without extracapsular spread, and margin status.

RESULTS

Of the 101 patients, 38 parotid, 33 palate, 17 oral, 8 submandibular/sublingual, 4 orbital, and 1 parapharyngeal carcinomas were identified. All patients underwent surgical resection, 23 had postoperative radiation, and 2 had postoperative chemoradiation. The 5-year OS and DSS was 79% and 95% with a median follow-up of 72 and 45 months, respectively. Five-year DFS was 76%. On univariate analysis, histological grade was a statistically significant predictor of disease-free survival (P = .001) and overall survival (P = .04). Positive nodal status was a significant predictor of DSS (P = .004). There was no statistically significant difference in DFS, DSS, or OS based on sex, age, anatomic subsite, T stage, adjuvant therapy, and margin status.

CONCLUSION

Advanced histological grade and positive nodal status are the strongest independent predictors of prognosis in head and neck MEC patients. Further studies into the molecular biology of MEC that may account for such clinicopathological features are currently underway.

Digital imaging in pathology: whole-slide imaging and beyond

Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.