Delays in oral cavity cancer

Gigliotti, Jordan; Madathil, Sreenath; Makhoul, Nicholas

doi:10.1016/j.ijom.2019.02.015

Cited by 79 publications

(83 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Early detection of OCSCC is challenging because of poor public awareness and knowledge about oral cancer, particularly its clinical presentation. Furthermore, it is really hard for patients and even non-specialist healthcare professionals to perceive subtle visual signs of OCSCC from the variability in the appearance of oral mucosa lesions [ 11 , 12 ]. For example, appearances of tumours could be erythematous and ulcerative lesions since the onset, typically producing no prominent signs and discomfort until they progress.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

et al. 2020

View full text Add to dashboard Cite

Background The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers’ clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. Methods We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. Findings 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973–0·991), sensitivity of 94·9% (0·915–0·978), and specificity of 88·7% (0·845–0·926) on the internal validation dataset ( n = 401), and an AUC of 0·935 (0·910–0·957), sensitivity of 89·6% (0·847–0·942) and specificity of 80·6% (0·757–0·853) on the external validation dataset ( n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988–0·999), sensitivity of 97·4% (0·932–1·000) and specificity of 93·5% (0·882–0·979) in detecting early-stage OCSCC. On the clinical validation dataset ( n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902–0·943] vs 92.4% [0·912–0·936]), sensitivity (91·0% [0·879–0·941] vs 91·7% [0·898–0·934]), and specificity (93·5% [0·909–0·960] vs 93·1% [0·914–0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855–0·885], sensitivity of 83·1% [0·807–0·854], and specificity of 90·7% [0·889–0·924]) and the average non-medical student (accuracy of 77·2% [0...

show abstract

Section: Discussionmentioning

confidence: 99%

“…The diagnosis of OCSCC has to rely on invasive oral biopsy which is not only time-consuming, but also not guaranteed in primary care or community settings, especially in developing countries [ 9 , 10 ]. Thus, quite often OCSCC patients cannot receive timely diagnosis and referrals [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…More than 50% of oral cancer cases are diagnosed at advanced clinical stages (12). Delay in diagnosis is common and its definition usually includes patient delay, professional delay and treatment delay (13). Among these three types of delay, the patient delay is the most significant for total delay in diagnosis of oral cancer (14).…”

Section: Discussionmentioning

confidence: 99%

Awareness on oral cancer among patients attending dental school clinics in Brazil

Rf²,

Ac³

et al. 2020

Med Oral

View full text Add to dashboard Cite

show abstract

“…In 2018, the incidence of oral cancer was estimated to be 354,900 cases, resulting in 177,400 deaths worldwide (Ferlay et al, 2019). On diagnosis, most patients were present with the disease at an advanced clinical stage (Gigliotti, Madathil, & Makhoul, 2019). Delay in diagnosis is common and may be attributed to factors associated with the patient, health care provider, and treatment (Güneri & Epstein, 2014).…”

Section: Dear Editormentioning

confidence: 99%