Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective

Paderno, Alberto; Piazza, Cesare; Bon, Francesca Del; Lancini, Davide; Tanagli, Stefano; Deganello, Alberto; Peretti, Giorgio; Momi, Elena De; Patrini, Ilaria; Ruperti, Michela; Mattos, Leonardo S.; Moccia, Sara

doi:10.3389/fonc.2021.626602

Cited by 41 publications

(36 citation statements)

References 26 publications

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“…A recent systematic review showed that the vascular changes suffered in the chorion and submucosa capillary loop microvascular architecture, observed through narrow-band imaging (NBI), provide greater reliably for the diagnosis of premalignant oral lesions and oral cancer than using white-light imaging [ 69 ]. Segmentation of NBI videos by AI has been used for the diagnosis of oropharyngeal cancer [ 70 , 71 ] and for oral precancer and cancer [ 72 ]. Paderno et al, in a publication this year, stated that by applying the fully convoluted neural network for the segmentation of video-endoscopic images, values of 0.6559 could be obtained for the dice similarity coefficient [ 72 ], so despite not having been included in the present study, the NBI also seems a promising tool for the diagnosis of oral cancer.…”

Section: Discussionmentioning

confidence: 99%

“…Segmentation of NBI videos by AI has been used for the diagnosis of oropharyngeal cancer [ 70 , 71 ] and for oral precancer and cancer [ 72 ]. Paderno et al, in a publication this year, stated that by applying the fully convoluted neural network for the segmentation of video-endoscopic images, values of 0.6559 could be obtained for the dice similarity coefficient [ 72 ], so despite not having been included in the present study, the NBI also seems a promising tool for the diagnosis of oral cancer.…”

Section: Discussionmentioning

confidence: 99%

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Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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“…When the 2 wavelengths are combined, the blood vessels on the tissue surface can be observed. Many studies have confirmed the value of NBI endoscopy in the diagnosis of laryngeal diseases (10)(11)(12)(13)(14). It is especially superior to fibrolaryngoscope, stroboscope, and other examination methods in the diagnosis of benign and malignant lesions.…”

Section: Introductionmentioning

confidence: 93%

A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis

He¹,

Cheng²,

Huang³

et al. 2021

Ann Transl Med

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Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common tumors of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy analysis and pathological findings. Deep-learning algorithms have been shown to provide accurate clinical diagnoses. Methods: We developed a deep convolutional neural network (CNN) model, and evaluated its application to narrow-band imaging (NBI) endoscopy and pathological diagnoses of LSCC at several hospitals. A total of 4,591 patients' laryngeal NBI scans (1,927 benign and 2,664 LSCC) were used to test and validate the model. Additionally, 3,458 pathological images (752 benign and 2,706 LSCC) of 1,228 patients' hematoxylin and eosin staining slides (318 benign and 910 LSCC) were used for the pathological diagnosis training and validation. The images were randomly divided into training, validation and testing images at the ratio of 70:15:15. An independent test cohort of LSCC NBI scans and pathological images from other institutions were also used.Results: In the NBI group, the areas under the curve of the validation, test, and independent test data sets were 0.966, 0.964, and 0.873, respectively, and those of the pathology group were 0.994, 0.981, and 0.982, respectively. Our method was highly accurate at diagnosing LSCC.Conclusions: In this study, the CNN model performed well in the NBI and pathological diagnosis of LSCC. More accurate and faster diagnoses could be achieved with the assistance of this algorithm.

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“…Initially, this was pursued through texture analysis of NBI image patches ( Moccia et al, 2017 ), which achieved a median recall of 98% on a well-balanced dataset built from endoscopic videos of 33 patients. Subsequently, with the development of Deep Learning methods, the work progressed to the automatic analysis, segmentation and classification of tumors in full endoscopic video frames ( Paderno et al, 2021a ).…”

Section: Micro-technologies and Systems For Robot-assisted Endoscopic Laser Microsurgerymentioning

confidence: 99%

μRALP and Beyond: Micro-Technologies and Systems for Robot-Assisted Endoscopic Laser Microsurgery

et al. 2021

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Laser microsurgery is the current gold standard surgical technique for the treatment of selected diseases in delicate organs such as the larynx. However, the operations require large surgical expertise and dexterity, and face significant limitations imposed by available technology, such as the requirement for direct line of sight to the surgical field, restricted access, and direct manual control of the surgical instruments. To change this status quo, the European project μRALP pioneered research towards a complete redesign of current laser microsurgery systems, focusing on the development of robotic micro-technologies to enable endoscopic operations. This has fostered awareness and interest in this field, which presents a unique set of needs, requirements and constraints, leading to research and technological developments beyond μRALP and its research consortium. This paper reviews the achievements and key contributions of such research, providing an overview of the current state of the art in robot-assisted endoscopic laser microsurgery. The primary target application considered is phonomicrosurgery, which is a representative use case involving highly challenging microsurgical techniques for the treatment of glottic diseases. The paper starts by presenting the motivations and rationale for endoscopic laser microsurgery, which leads to the introduction of robotics as an enabling technology for improved surgical field accessibility, visualization and management. Then, research goals, achievements, and current state of different technologies that can build-up to an effective robotic system for endoscopic laser microsurgery are presented. This includes research in micro-robotic laser steering, flexible robotic endoscopes, augmented imaging, assistive surgeon-robot interfaces, and cognitive surgical systems. Innovations in each of these areas are shown to provide sizable progress towards more precise, safer and higher quality endoscopic laser microsurgeries. Yet, major impact is really expected from the full integration of such individual contributions into a complete clinical surgical robotic system, as illustrated in the end of this paper with a description of preliminary cadaver trials conducted with the integrated μRALP system. Overall, the contribution of this paper lays in outlining the current state of the art and open challenges in the area of robot-assisted endoscopic laser microsurgery, which has important clinical applications even beyond laryngology.

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Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective

Cited by 41 publications

References 26 publications

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis

μRALP and Beyond: Micro-Technologies and Systems for Robot-Assisted Endoscopic Laser Microsurgery

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