Abstract:Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with the Ki-67 index has great impact on tumor microenvironment, therapeutic strategy making, and patients’ prognosis. However, the most commonly used method to obtain the proliferation status is through biopsy or surgical immunohistochemical staining. Noninvasive method before operation remains a challenge. Hence, in this study, we aimed to validate a novel method to predict the proli… Show more
“…After this screening process, 12 articles were selected, and the data were retrieved, as shown in Table 1 . 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 Fig. 1 PRISMA flowchart.…”
Section: Resultsmentioning
confidence: 99%
“… Oral cavity images acquired using smartphones Pre-trained CNNs are used for identifying oral pre-cancerous and cancerous lesions and to differentiate them from normal mucosa using a dataset of clinically annotated photographic images Study demonstrated the performance of CNN models in identification and classification comparable to a biopsy report Sun et al. (2022) 17 To validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on AI CECT images of the lesion area from 179 TSCC patients Sample analyzed using a CNN Study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation Warin (2021) 20 To use the CNN deep learning algorithms to develop an automated classification and detection model for oral cancer screening. A total of 700 clinical oral photographs were collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa Classification and detection models were created by using DenseNet121 and faster R-CNN, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…suggested that the proliferation status of tongue squamous cell carcinoma could be non-invasively predicted using AI in CECT prior to operation. 17 Ilhan et al. reported that the introduction of numerous new omics technologies, such as genomics and proteomics, made it possible to collect vast quantities of cancer data.…”
“…After this screening process, 12 articles were selected, and the data were retrieved, as shown in Table 1 . 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 Fig. 1 PRISMA flowchart.…”
Section: Resultsmentioning
confidence: 99%
“… Oral cavity images acquired using smartphones Pre-trained CNNs are used for identifying oral pre-cancerous and cancerous lesions and to differentiate them from normal mucosa using a dataset of clinically annotated photographic images Study demonstrated the performance of CNN models in identification and classification comparable to a biopsy report Sun et al. (2022) 17 To validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on AI CECT images of the lesion area from 179 TSCC patients Sample analyzed using a CNN Study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation Warin (2021) 20 To use the CNN deep learning algorithms to develop an automated classification and detection model for oral cancer screening. A total of 700 clinical oral photographs were collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa Classification and detection models were created by using DenseNet121 and faster R-CNN, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…suggested that the proliferation status of tongue squamous cell carcinoma could be non-invasively predicted using AI in CECT prior to operation. 17 Ilhan et al. reported that the introduction of numerous new omics technologies, such as genomics and proteomics, made it possible to collect vast quantities of cancer data.…”
Machine learning models have been increasingly considered to model head and neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of the disease. As the concept of data-centric artificial intelligence is still incipient in healthcare systems, little is known about the data quality of the models proposed for clinical utility. This is important as it supports the generalizability of the models and data standardization. Therefore, this study overviews the quality of structured and unstructured data used for machine learning model construction in head and neck cancer. Relevant studies reporting on the use of machine learning models based on structured and unstructured custom datasets between January 2016 and June 2022 were sourced from PubMed, EMBASE, Scopus, and Web of Science electronic databases. Prediction model Risk of Bias Assessment (PROBAST) tool was used to assess the quality of individual studies before comprehensive data quality parameters were assessed according to the type of dataset used for model construction. A total of 159 studies were included in the review; 106 utilized structured datasets while 53 utilized unstructured datasets. Data quality assessments were deliberately performed for 14.2% of structured datasets and 11.3% of unstructured datasets before model construction. Class imbalance and data fairness were the most common limitations in data quality for both types of datasets while outlier detection and lack of representative outcome classes were common in structured and unstructured datasets respectively. Furthermore, this review found that class imbalance reduced the discriminatory performance for models based on structured datasets while higher image resolution and good class overlap resulted in better model performance using unstructured datasets during internal validation. Overall, data quality was infrequently assessed before the construction of ML models in head and neck cancer irrespective of the use of structured or unstructured datasets. To improve model generalizability, the assessments discussed in this study should be introduced during model construction to achieve data-centric intelligent systems for head and neck cancer management.
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