Surgery is not the only determinant of an outcome in patients with hypopharyngeal carcinoma

Abstract

Background The aim of this study was to evaluate the outcomes in patients treated for hypopharyngeal carcinoma in a single‐center and the importance of considering how patient factors influence outcomes. Methods A retrospective review was conducted on patients who were seen at the Prince of Wales Hospital from 1968 to 2015. Kaplan‐Meier and Cox regression analyses were performed for each patient and treatment factor to investigate outcomes of local control, cancer‐specific survival (CSS), and ultimate local control. Results Three hundred thirty patients were analyzed. Significant multivariate predictors for improved local control and CSS were fitness for surgery, cancer operability, surgery with adjuvant radiotherapy, no treatment interruptions (P < .05). Five‐year local control (84%), CSS (50%), and ultimate local control (84%) rates were significantly higher in patients treated surgically with adjuvant radiotherapy, compared to single modality treatment (P < .05). Conclusion Patient factors influence the outcomes experienced by patients with hypopharyngeal carcinoma.

Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models

“…Our results add to this body of work, whereby patients with salivary gland, oral cavity, and hypopharynx cancer have worse 2‐year CSS compared to patients with oropharynx cancer. Fitness for operation is an important patient factor which influences hypopharyngeal CSS 1 ; however, our results demonstrate that this feature may also be important in other head and neck subsites, warranting further investigation. Our study demonstrated that a high proportion of patients classified as unfit (74%) and fit for surgery (98%) had good performance status (0–1).…”

“…The OIS dataset contained routinely collected data for government reporting, extracted using structured query language. The research dataset contained manually curated data extracted from unstructured clinical documents and has been use in several publications 1–3,16–18 …”

“…The research dataset contained manually curated data extracted from unstructured clinical documents and has been use in several publications. [1][2][3][16][17][18] Death data were sourced from the National Death Index (NDI) in February 2018 via probabilistic record linkage (EO2017/5/392), the NSW Registry of Births, Death and Marriages, hospital notifications, and the Ryerson Index (death notices and obituaries in Australian newspapers). NDI cause of death data were not available for patients registered as deceased in the 2 years prior to linkage (2016-2017).…”

“…Cancer‐specific survival (CSS) is a critical oncological outcome measure used to understand the impact of cancer treatment. 1 , 2 , 3 Machine learning (ML) prediction of 2‐year CSS in the head and neck cancer (HNC) population, at the site and subsite level, is relatively unexplored due to difficulties accessing death certificate data in hospital settings.…”

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer

“…In NSW, cancer data can be accessed from the Australian Cancer Database [ 3 ], or the NSW Central Cancer Registry [ 4 ], however, neither resource includes the clinical detail necessary to predict HNC survival. Unavailable prognostic factors include smoking history [ 5 ], comorbidities [ 6 ], human papillomavirus infection [ 5 , 7 ], fitness for surgery [ 8 ], cancer operability [ 8 ], size and location of involved lymph nodes in the neck [ 5 , 9 , 10 ], and radiotherapy dose and duration [ 5 , 8 , 11 , 12 ].…”