Utilizing a publicly accessible automated machine learning platform to enable diagnosis before tumor surgery

In benign tumors with potential for malignant transformation, sampling error during pre-operative biopsy can significantly change patient counseling and surgical planning. Sinonasal inverted papilloma (IP) is the most common benign soft tissue tumor of the sinuses, yet it can undergo malignant transformation to squamous cell carcinoma (IP-SCC), for which the planned surgery could be drastically different. Artificial intelligence (AI) could potentially help with this diagnostic challenge. CT images from 19 institutions were used to train the Google Cloud Vertex AI platform to distinguish between IP and IP-SCC. The model was evaluated on a holdout test dataset of images from patients whose data were not used for training or validation. Performance metrics of area under the curve (AUC), sensitivity, specificity, accuracy, and F1 were used to assess the model. Here we show CT image data from 958 patients and 41099 individual images that were labeled to train and validate the deep learning image classification model. The model demonstrated a 95.8 % sensitivity in correctly identifying IP-SCC cases from IP, while specificity was robust at 99.7 %. Overall, the model achieved an accuracy of 99.1%. A deep automated machine learning model, created from a publicly available artificial intelligence tool, using pre-operative CT imaging alone, identified malignant transformation of inverted papilloma with excellent accuracy. Planning for surgery to remove a tumor, and the preoperative counseling a surgeon gives to the patient, can be very different, depending on if that tumor is cancerous or non-cancerous. Unfortunately, it can be difficult to always know which it is before actually getting to the operating room. Here we show the utilization of a publicly available platform, Google Vertex AI, and pre-operative computed tomography (CT) imaging of patients from nineteen separate institutions, to identify cancerous transformation of a non-cancerous tumor with excellent accuracy in a specific tumor type. An automated machine learning (AutoML) model, created from a publicly available artificial intelligence tool, by physicians with little coding background, was able to differentiate between these types of tumors with better accuracy than previously published rates from experts. This tool could serve to better inform surgical planning for tumors. Hosseinzadeh et al. demonstrate use of a publicly accessible automated machine learning platform to differentiate between a common benign tumor and malignant transformation of it within the paranasal sinuses. This AI algorithm beat prior human prediction, and showed that physicians with no coding background can effectively utilize this tool.

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