1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          The conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.

          Objective

          This study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.

          Methods

          Five models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.

          Results

          A total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72–0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61–0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73–0.96).

          Conclusion

          BITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

            Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies.A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Head and neck squamous cell carcinoma

              Most head and neck cancers are derived from the mucosal epithelium in the oral cavity, pharynx and larynx and are known collectively as head and neck squamous cell carcinoma (HNSCC). Oral cavity and larynx cancers are generally associated with tobacco consumption, alcohol abuse or both, whereas pharynx cancers are increasingly attributed to infection with human papillomavirus (HPV), primarily HPV-16. Thus, HNSCC can be separated into HPV-negative or HPV-positive HNSCC. Despite evidence of histological progression from cellular atypia through various degrees of dysplasia, ultimately leading to invasive HNSCC, most patients are diagnosed with late-stage HNSCC without a clinically evident antecedent premalignant lesion. Traditional staging of HNSCC using the tumour-node-metastasis system has been supplemented by the 2017 AJCC/UICC staging system, which incorporated additional information relevant to HPV-positive disease. The treatment approach is generally multimodal, consisting of surgery followed by chemotherapy plus radiation (chemoradiation or CRT) for oral cavity cancers and primary CRT for pharynx and larynx cancers. The EGFR monoclonal antibody cetuximab is generally used in combination with radiation in HPV-negative HNSCC where co-morbidities prevent the use of cytotoxic chemotherapy. The FDA approved the immune checkpoint inhibitors pembrolizumab and nivolumab for treatment of recurrent or metastatic HNSCC and pembrolizumab as primary treatment for unresectable disease. Elucidation of the molecular genetic landscape of HNSCC over the past decade has revealed new opportunities for therapeutic intervention. Ongoing efforts aim to integrate our understanding of HNSCC biology and immunobiology to identify predictive biomarkers that will enable delivery of the most effective, least toxic therapies.
                Bookmark

                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                URI : http://loop.frontiersin.org/people/2088461/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role:
                Role: Role: Role:
                Role: Role:
                Role: Role:
                Role: Role: Role:
                URI : http://loop.frontiersin.org/people/2814337/overviewRole: Role: Role:
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                06 January 2025
                2024
                : 11
                : 1478842
                Affiliations
                [1] 1Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Tongji Research Institute of Stomatology, Department of Prosthodontics, Shanghai Tongji Stomatological Hospital, Dental School, Tongji University , Shanghai, China
                [2] 2School of Medicine, Tongji University , Shanghai, China
                [3] 3Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Tongji Research Institute of Stomatology, Department of Periodontics, Shanghai Tongji Stomatological Hospital, Dental School, Tongji University , Shanghai, China
                [4] 4Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Tongji Research Institute of Stomatology, Department of Oral and Maxillofacial Surgery, Shanghai Tongji Stomatological Hospital, Dental School, Tongji University , Shanghai, China
                [5] 5Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Tongji Research Institute of Stomatology, Department of Oral Implantology, Shanghai Tongji Stomatological Hospital, Dental School, Tongji University , Shanghai, China
                [6] 6Department of Medical Statistics, School of Medicine, Tongji University , Shanghai, China
                Author notes

                Edited by: Fujun Han, The First Hospital of Jilin University, China

                Reviewed by: Renjie He, University of Texas MD Anderson Cancer Center, United States

                Haitao Zhu, Peking University, China

                *Correspondence: Zisheng Ai azs1966@ 123456126.com

                †These authors have contributed equally to this work and share first authorship

                ‡These authors have contributed equally to this work

                §ORCID: Linmei Zhang orcid.org/0009-0003-1132-8597

                Article
                10.3389/fmed.2024.1478842
                11744519
                39835092
                9067d4ae-f9a4-4353-a51f-4007c563e935
                Copyright © 2025 Zhang, Zhu, Shi, Wu, Cao, Huang, Ai and Su.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 August 2024
                : 28 November 2024
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 45, Pages: 13, Words: 7183
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by grants from the National Natural Science Foundation of China (Grant No. 81873715).
                Categories
                Medicine
                Original Research
                Custom metadata
                Precision Medicine

                head and neck squamous cell carcinoma,chemoradiotherapy,deep learning,causal inference,precise medicine

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content153

                Most referenced authors1,224