Proof-of-principle study using

Screening strategies for cancer, the second largest cause of deaths, exist, but are invasive, cumbersome, and expensive. Many cancers lack viable screening modalities all together. Circulating tumor cell clusters (CTCCs) are seen during early stages of cancer and are larger than normal blood cells. Discrimination of such differential sizes by real-time ultrasound scanning of a blood vessel offers an attractive universal screening tool for cancer. Yeast colonies were grown to different sizes mimicking CTCCs and normal blood cells, using sugar and starch to incubate and sodium fluoride to arrest growth after specified times. They were circulated using syringes and an infusion pump through a wall-less ultrasound phantom, made using agar (mimicking human soft tissue), and Doppler ultrasound was performed, with screenshots taken. Key characteristics of particles of interest were identified. Ultrasound data were processed and used to train a convolutional neural network (CNN). Six models with binary classification were tested. Doppler signals of CTCC surrogates could be visually distinguished from normal cells and normal saline, proving the principle of ultrasound size discrimination of CTCCs. The most accurate machine learning model yielded 98.35% accuracy in the prediction of CTCCs, exceeding human evaluation accuracy. Thus, machine learning could help automate and improve detection of cancer by screening for CTCCs.