TY - JOUR
T1 - Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma
AU - Farrokhian, Nathan
AU - Holcomb, Andrew J.
AU - Dimon, Erin
AU - Karadaghy, Omar
AU - Ward, Christina
AU - Whiteford, Erin
AU - Tolan, Claire
AU - Hanly, Elyse K.
AU - Buchakjian, Marisa R.
AU - Harding, Brette
AU - Dooley, Laura
AU - Shinn, Justin
AU - Wood, C. Burton
AU - Rohde, Sarah L.
AU - Khaja, Sobia
AU - Parikh, Anuraag
AU - Bulbul, Mustafa G.
AU - Penn, Joseph
AU - Goodwin, Sara
AU - Bur, Andrés M.
N1 - Publisher Copyright:
© 2022 American Medical Association. All rights reserved.
PY - 2022/4/13
Y1 - 2022/4/13
N2 - Importance: Given that early-stage oral cavity squamous cell carcinoma (OCSCC) has a high propensity for subclinical nodal metastasis, elective neck dissection has become standard practice for many patients with clinically negative nodes. Unfortunately, for most patients without regional metastasis, this risk-averse treatment paradigm results in unnecessary morbidity.Objectives: To develop and validate predictive models of occult nodal metastasis from clinicopathological variables that were available after surgical extirpation of the primary tumor and to compare predictive performance against depth of invasion (DOI), the currently accepted standard.Design, Setting, and Participants: This diagnostic modeling study collected clinicopathological variables retrospectively from 7 tertiary care academic medical centers across the US. Participants included adult patients with early-stage OCSCC without nodal involvement who underwent primary surgical extirpation with or without upfront elective neck dissection. These patients were initially evaluated between January 1, 2000, and December 31, 2019.Exposures: Largest tumor dimension, tumor thickness, DOI, margin status, lymphovascular invasion, perineural invasion, muscle invasion, submucosal invasion, dysplasia, histological grade, anatomical subsite, age, sex, smoking history, race and ethnicity, and body mass index (calculated as weight in kilograms divided by height in meters squared).Main Outcomes and Measures: Occult nodal metastasis identified either at the time of elective neck dissection or regional recurrence within 2 years of initial surgery.Results: Of the 634 included patients (mean [SD] age, 61.2 [13.6] years; 344 men [54.3%]), 114 (18.0%) had occult nodal metastasis. Patients with occult nodal metastasis had a higher frequency of lymphovascular invasion (26.3% vs 8.1%; P < .001), perineural invasion (40.4% vs 18.5%; P < .001), and margin involvement by invasive tumor (12.3% vs 6.3%; P = .046) compared with those without pathological lymph node metastasis. In addition, patients with vs those without occult nodal metastasis had a higher frequency of poorly differentiated primary tumor (20.2% vs 6.2%; P < .001) and greater DOI (7.0 vs 5.4 mm; P < .001). A predictive model that was built with XGBoost architecture outperformed the commonly used DOI threshold of 4 mm, achieving an area under the curve of 0.84 (95% CI, 0.80-0.88) vs 0.62 (95% CI, 0.57-0.67) with DOI. This model had a sensitivity of 91.7%, specificity of 72.6%, positive predictive value of 39.3%, and negative predictive value of 97.8%.Conclusions and Relevance: Results of this study showed that machine learning models that were developed from multi-institutional clinicopathological data have the potential to not only reduce the number of pathologically node-negative neck dissections but also accurately identify patients with early OCSCC who are at highest risk for nodal metastases.
AB - Importance: Given that early-stage oral cavity squamous cell carcinoma (OCSCC) has a high propensity for subclinical nodal metastasis, elective neck dissection has become standard practice for many patients with clinically negative nodes. Unfortunately, for most patients without regional metastasis, this risk-averse treatment paradigm results in unnecessary morbidity.Objectives: To develop and validate predictive models of occult nodal metastasis from clinicopathological variables that were available after surgical extirpation of the primary tumor and to compare predictive performance against depth of invasion (DOI), the currently accepted standard.Design, Setting, and Participants: This diagnostic modeling study collected clinicopathological variables retrospectively from 7 tertiary care academic medical centers across the US. Participants included adult patients with early-stage OCSCC without nodal involvement who underwent primary surgical extirpation with or without upfront elective neck dissection. These patients were initially evaluated between January 1, 2000, and December 31, 2019.Exposures: Largest tumor dimension, tumor thickness, DOI, margin status, lymphovascular invasion, perineural invasion, muscle invasion, submucosal invasion, dysplasia, histological grade, anatomical subsite, age, sex, smoking history, race and ethnicity, and body mass index (calculated as weight in kilograms divided by height in meters squared).Main Outcomes and Measures: Occult nodal metastasis identified either at the time of elective neck dissection or regional recurrence within 2 years of initial surgery.Results: Of the 634 included patients (mean [SD] age, 61.2 [13.6] years; 344 men [54.3%]), 114 (18.0%) had occult nodal metastasis. Patients with occult nodal metastasis had a higher frequency of lymphovascular invasion (26.3% vs 8.1%; P < .001), perineural invasion (40.4% vs 18.5%; P < .001), and margin involvement by invasive tumor (12.3% vs 6.3%; P = .046) compared with those without pathological lymph node metastasis. In addition, patients with vs those without occult nodal metastasis had a higher frequency of poorly differentiated primary tumor (20.2% vs 6.2%; P < .001) and greater DOI (7.0 vs 5.4 mm; P < .001). A predictive model that was built with XGBoost architecture outperformed the commonly used DOI threshold of 4 mm, achieving an area under the curve of 0.84 (95% CI, 0.80-0.88) vs 0.62 (95% CI, 0.57-0.67) with DOI. This model had a sensitivity of 91.7%, specificity of 72.6%, positive predictive value of 39.3%, and negative predictive value of 97.8%.Conclusions and Relevance: Results of this study showed that machine learning models that were developed from multi-institutional clinicopathological data have the potential to not only reduce the number of pathologically node-negative neck dissections but also accurately identify patients with early OCSCC who are at highest risk for nodal metastases.
KW - Adult
KW - Carcinoma, Squamous Cell/pathology
KW - Female
KW - Head and Neck Neoplasms
KW - Humans
KW - Machine Learning
KW - Male
KW - Middle Aged
KW - Mouth Neoplasms/pathology
KW - Retrospective Studies
KW - Squamous Cell Carcinoma of Head and Neck
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U2 - 10.1001/jamanetworkopen.2022.7226
DO - 10.1001/jamanetworkopen.2022.7226
M3 - Article
C2 - 35416990
AN - SCOPUS:85128127897
SN - 2574-3805
VL - 5
SP - E227226
JO - JAMA Network Open
JF - JAMA Network Open
IS - 4
ER -