Evaluation of Locoregional Recurrence Patterns Following Adjuvant (Chemo)Radiotherapy for Oral Cavity Carcinoma

Waldram, R.; Taylor, Arthur J.; Whittam, S.; Iyizoba-Ebozue, Zsuzsanna; Murray, Louise; Frood, Russell; Cardale, K.; Dyker, K.; Murray, P.; Ramasamy, S.; Şen, Mehmet; Al-Qaisieh, B.; Prestwich, R.

doi:10.1016/j.clon.2019.10.002

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…In recent years, demographic, clinicopathological, therapeutic and bio‐molecular data have all been used to populate clinical decision‐making tools, including statistical regression models and prognostic nomograms, in an attempt to predict poor clinical outcome post‐OSCC treatment. Unfortunately, such methods have gained limited acceptance in contemporary clinical practice due to data validity concerns and little demonstrable predictive accuracy 3‐12 …”

Section: Introductionmentioning

confidence: 99%

Machine learning and treatment outcome prediction for oral cancer

Chu

Lee

Adeoye

et al. 2020

J Oral Pathology Medicine

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Background The natural history of oral squamous cell carcinoma (OSCC) is complicated by progressive disease including loco‐regional tumour recurrence and development of distant metastases. Accurate prediction of tumour behaviour is crucial in delivering individualized treatment plans and developing optimal patient follow‐up and surveillance strategies. Machine learning algorithms may be employed in oncology research to improve clinical outcome prediction. Methods Retrospective review of 467 OSCC patients treated over a 19‐year period facilitated construction of a detailed clinicopathological database. 34 prognostic features from the database were used to populate 4 machine learning algorithms, linear regression (LR), decision tree (DT), support vector machine (SVM) and k‐nearest neighbours (KNN) models, to attempt progressive disease outcome prediction. Principal component analysis (PCA) and bivariate analysis were used to reduce data dimensionality and highlight correlated variables. Models were validated for accuracy, sensitivity and specificity, with predictive ability assessed by receiver operating characteristic (ROC) and area under the curve (AUC) calculation. Results Out of 408 fully characterized OSCC patients, 151 (37%) had died and 131 (32%) exhibited progressive disease at the time of data retrieval. The DT model with 34 prognostic features was most successful in identifying “true positive” progressive disease, achieving 70.59% accuracy (AUC 0.67), 41.98% sensitivity and a high specificity of 84.12%. Conclusion Machine learning models assist clinicians in accessing digitized health information and appear promising in predicting progressive disease outcomes. The future will see increasing emphasis on the use of artificial intelligence to enhance understanding of aggressive tumour behaviour, recurrence and disease progression.

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Section: Introductionmentioning

confidence: 99%

Machine learning and treatment outcome prediction for oral cancer

Chu

Lee

Adeoye

et al. 2020

J Oral Pathology Medicine

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“…After removing duplicates, 2345 abstracts were screened, and 114 articles underwent full text review (Figure 1). In total, 15 studies ( n = 1825) were included for statistical analysis 31–45 . Baseline characteristics, treatments, and outcomes from the included studies are summarized in Table S1–S6, Supporting Information.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, studies that treated buccal, gingival, or retromolar trigone tumors (i.e., more lateralized tumor sites) appeared to favor ipsilateral RT, 38,43,44 while studies treating predominantly oral tongue tumors (i.e., midline tumor sites) appeared to favor bilateral RT. 33,39,42 Overall, our study had several strengths. First, this is the largest analysis of outcomes following ipsilateral RT for OCSCC.…”

Section: Discussionmentioning

confidence: 99%

“…In total, 15 studies (n = 1825) were included for statistical analysis. [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] Baseline characteristics, treatments, and outcomes from the included studies are summarized in Table S1-S6, Supporting Information. Included studies were published from 2003 to 2021, most studies were retrospective (13 of 15 studies) and had median follow-up ranging from 20 to 66 months.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

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Association of ipsilateral radiation therapy with contralateral lymph node failure in patients with squamous cell carcinoma of the oral cavity: A systematic review and meta‐analysis

et al. 2023

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Background Ipsilateral neck radiotherapy (INRT) is controversial in some patients with oral cavity cancer due to concern for contralateral neck failure (CNF). Methods A systematic review was performed and data were extracted following PRISMA guidelines. Outcomes were the rate of CNF following INRT and the rates of CNF by AJCC 7th ed. tumor and nodal staging. Results Fifteen studies consisting of 1825 patients were identified. Among the 805 patients treated with INRT, the rate of CNF was 5.7%. Patients with T4 tumors constituted 56% of all CNF cases. The rate of CNF increased by N stage (N0: 1.2%; N1: 3.8%; N2‐N3: 17.4%) and was significantly higher for patients with N2‐N3 than N0‐N1 disease (p < 0.001). Discussion INRT is associated with an overall low risk of CNF in well‐selected patients with N0‐N1 disease. Patients with N2‐3 and/or T4 disease should receive bilateral RT due to increased risk of CNF following INRT.

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“…Recent retrospective data reported a low contralateral neck recurrence rate in patients with head and neck cancers. Table 3 lists a literature review of contralateral neck failure rate in primary head and neck cancers, particularly cancer at the oral cavity sites [ 5 , 11 , 12 , 13 , 14 , 15 , 20 ]. Unlike most of the historical analysis of mixed head and neck cancers and relatively early-stage OSCC, our study focused on a homogeneous patient series with well-lateralized and more advanced-stage OSCC to avoid confounding interactions between different origins of head and neck cancers.…”

Section: Discussionmentioning

confidence: 99%

Contralateral Neck Irradiation Can Be Omitted for Selected Lateralized Oral Cancer in Locally Advanced Stage

et al. 2022

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(1) Background: To investigate the contralateral neck failure (cRF) rates and outcomes among patients with well-lateralized locally advanced oral cavity squamous cell carcinoma (OSCC) with/without ipsilateral or bilateral neck adjuvant irradiation. (2) Methods: Patients with lateralized OSCC diagnosed between 2007 and 2017 were retrospectively enrolled. Patients who had undergone curative surgery with pathologically proven pT3/4 or pN0-2b without distant metastasis were included, while those with cross-midline, neck-level 1a involvement and positive extra-nodal extension (ENE) were excluded. The primary endpoint was the cumulative incidence of 5-year cRF as the first site of failure. The secondary endpoints included cancer-specific survival (CSS), local-regional recurrence-free survival (LRRFS), distant-metastasis-free survival (DMFS), and contralateral-regional recurrence-free survival (cRRFS). (3) Results: In total, 149 patients were analyzed with a median follow-up time of 5.2 years (range, 2.91–7.83). Pathological stages T3 and T4 were 22.7% and 56.8%, respectively. Pathologically negative and positive lymph nodes were 61.4% and 38.6%, respectively. The cumulative 5-year cRF rate was 3.6% (95% CI, 1.3–7.7%). No significant differences in the 5-year CSS, LRRFS, DMFS, and cRRFS were observed among those undergoing unilateral or bilateral neck irradiation. Five patients (3.4%) had contralateral neck recurrence, all simultaneously with local recurrence. No isolated contralateral neck recurrence was identified. (4) Conclusions: The cRF rate was acceptably low in patients with well-lateralized advanced OSCC with the initially uninvolved contralateral neck. Omitting contralateral neck irradiation with active surveillance could be considered without compromising the cure rate in locally advanced OSCC patients.

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Evaluation of Locoregional Recurrence Patterns Following Adjuvant (Chemo)Radiotherapy for Oral Cavity Carcinoma

Cited by 11 publications

References 23 publications

Machine learning and treatment outcome prediction for oral cancer

Machine learning and treatment outcome prediction for oral cancer

Association of ipsilateral radiation therapy with contralateral lymph node failure in patients with squamous cell carcinoma of the oral cavity: A systematic review and meta‐analysis

Contralateral Neck Irradiation Can Be Omitted for Selected Lateralized Oral Cancer in Locally Advanced Stage

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