Dataset Information

The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer.

ABSTRACT: Although surgical resection has been the primary treatment modality of solid tumors for decades, surgeons still rely on visual cues and palpation to delineate healthy from cancerous tissue. This may contribute to the high rate (up to 30%) of positive margins in head and neck cancer resections. Margin status in these patients is the most important prognostic factor for overall survival. In addition, second primary lesions may be present at the time of surgery. Although often unnoticed by the medical team, these lesions can have significant survival ramifications. We hypothesize that real-time fluorescence imaging can enhance intraoperative decision making by aiding the surgeon in detecting close or positive margins and visualizing unanticipated regions of primary disease. The purpose of this study was to assess the clinical utility of real-time fluorescence imaging for intraoperative decision making. Methods: Head and neck cancer patients (n = 14) scheduled for curative resection were enrolled in a clinical trial evaluating panitumumab-IRDye800CW for surgical guidance (NCT02415881). Open-field fluorescence imaging was performed throughout the surgical procedure. The fluorescence signal was quantified as signal-to-background ratios to characterize the fluorescence contrast of regions of interest relative to background. Results: Fluorescence imaging was able to improve surgical decision making in 3 cases (21.4%): identification of a close margin (n = 1) and unanticipated regions of primary disease (n = 2). Conclusion: This study demonstrates the clinical applications of fluorescence imaging on intraoperative decision making. This information is required for designing phase III clinical trials using this technique. Furthermore, this study is the first to demonstrate this application for intraoperative decision making during resection of primary tumors.

SUBMITTER: van Keulen S

PROVIDER: S-EPMC6581234 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer.

van Keulen Stan S Nishio Naoki N Fakurnejad Shayan S Birkeland Andrew A Martin Brock A BA Lu Guolan G Zhou Quan Q Chirita Stefania U SU Forouzanfar Tymour T Colevas A Dimitrios AD van den Berg Nynke S NS Rosenthal Eben L EL

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20190207 6

Although surgical resection has been the primary treatment modality of solid tumors for decades, surgeons still rely on visual cues and palpation to delineate healthy from cancerous tissue. This may contribute to the high rate (up to 30%) of positive margins in head and neck cancer resections. Margin status in these patients is the most important prognostic factor for overall survival. In addition, second primary lesions may be present at the time of surgery. Although often unnoticed by the medi ...[more]

PMID: 30733319

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Project description:ImportancePostoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.ObjectiveTo evaluate whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation.Design, setting, and participantsThis cohort study included patients diagnosed with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx from January 1, 2004, through December 31, 2016. Patients had resected disease and underwent adjuvant radiotherapy. Analysis was performed from October 1, 2019, through September 1, 2020. Patients were selected from the National Cancer Database, a hospital-based registry that captures data from more than 70% of newly diagnosed cancers in the United States. Three machine learning survival models were trained using 80% of the cohort, with the remaining 20% used to assess model performance.ExposuresReceipt of adjuvant chemoradiation or radiation alone.Main outcomes and measuresPatients who received treatment recommended by machine learning models were compared with those who did not. Overall survival for treatment according to model recommendations was the primary outcome. Secondary outcomes included frequency of recommendation for chemotherapy and chemotherapy benefit in patients recommended for chemoradiation vs radiation alone.ResultsA total of 33 527 patients (24 189 [72%] men; 28 036 [84%] aged ≤70 years) met the inclusion criteria. Median follow-up in the validation data set was 43.2 (interquartile range, 19.8-65.5) months. DeepSurv, neural multitask logistic regression, and survival forest models recommended chemoradiation for 17 589 (52%), 15 917 (47%), and 14 912 patients (44%), respectively. Treatment according to model recommendations was associated with a survival benefit, with a hazard ratio of 0.79 (95% CI, 0.72-0.85; P < .001) for DeepSurv, 0.83 (95% CI, 0.77-0.90; P < .001) for neural multitask logistic regression, and 0.90 (95% CI, 0.83-0.98; P = .01) for random survival forest models. No survival benefit for chemotherapy was seen for patients recommended to receive radiotherapy alone.Conclusions and relevanceThese findings suggest that machine learning models may identify patients with intermediate risk who could benefit from chemoradiation. These models predicted that approximately half of such patients have no added benefit from chemotherapy.

Dataset Information

The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer.

Publications

The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer.

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