Artificial intelligence system spots lung cancer before radiologists

Artificial intelligence system spots lung cancer before radiologists

Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study published in Nature Medicine.

This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists.

The deep-learning system also produced fewer false positives and fewer false negatives, which could lead to fewer unnecessary follow-up procedures and fewer missed tumors, if it were used in a clinical setting.

"Radiologists generally examine hundreds of two-dimensional images or 'slices' in a single CT scan but this new machine learning system views the lungs in a huge, single three-dimensional image," said study co-author. "AI in 3D can be much more sensitive in its ability to detect early lung cancer than the human eye looking at 2-D images. This is technically '4D' because it is not only looking at one CT scan, but two (the current and prior scan) over time.

"In order to build the AI to view the CTs in this way, you require an enormous computer system of Google-scale. The concept is novel but the actual engineering of it is also novel because of the scale."

"Our work examines ways AI can be used to improve the accuracy and optimize the screening process, in ways that could help with the implementation of screening programs. The results are promising, and we look forward to continuing our work with partners and peers."

The deep-learning system utilizes both the primary CT scan and, whenever available, a prior CT scan from the patient as input. Prior CT scans are useful in predicting lung cancer malignancy risk because the growth rate of suspicious lung nodules can be indicative of malignancy. The computer was trained using fully de-identified, biopsy-confirmed low-dose chest CT scans.

The novel system identifies both a region of interest and whether the region has a high likelihood of lung cancer. The model outperformed six radiologists when previous CT imaging was not available and performed as well as the radiologists when there was prior imaging.

"The system can categorize a lesion with more specificity. Not only can we better diagnose someone with cancer, we can also say if someone doesn't have cancer, potentially saving them from an invasive, costly and risky lung biopsy," the author said.

The authors caution that these findings need to be clinically validated in large patient populations, but they say this model may assist in improving the management and outcome of patients with lung cancer.

https://www.nature.com/articles/s41591-019-0447-x

Edited

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