artificial intelligence (AI)-based deep learning technology has been
technology can detect lung cancer faster and more accurately than
experienced radiologists and make diagnosis easier
has the potential to save the lives of many lung cancer patients
Artificial intelligence (AI) using deep learning
technology, has successfully detected malignant lung cancer by analyzing
low-dose chest computed tomography (LDCT) scans with a performance that is at
par or better than that of expert radiologists, reports a new study conducted
by Google and Northwestern Medicine, USA. The study has been published in Nature Medicine
The study was led by Dr. Mozziyar Etemadi,
MD, PhD, who is a Research Assistant Professor in Biomedical Engineering and
Anesthesiology at the McCormick School of Engineering and Anesthesiology,
Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
‘New AI-based deep learning technology, which detects lung cancer faster and more accurately than radiologists, will make diagnosis easier and help save many lives.’
Etemadi's collaborator was Shravya
Shetty, who is a Senior Staff Software Engineer and Engineering Manager at
Google, Mountain View, California, USA. The corresponding author on
the paper was Dr. Daniel Tse, MD, who is a Product Manager at Google Brain, San
How Does the Deep Learning System Work?
, also known as
deep structured learning or hierarchical learning, is a type of machine
learning technique that involves artificial neural networks. It teaches
computers how to learn by example. In the present study, the power of deep
learning technology was harnessed for automated evaluation of images for early diagnosis of lung cancer
, enabling prompt
The deep learning system was used for evaluating LDCT
scans of de-identified patients with biopsy-confirmed lung cancer and its
accuracy and efficiency was compared to that of experienced radiologists. In
most instances, the precision of the new technology was equal to or better than
that of the radiologists.
The deep learning system used a primary CT scan and a
prior CT scan of the patient's lungs for comparison purposes, so that the
chances of occurrence of malignant lung cancer could be accurately predicted.
This novel technology not only identifies a region of the scan that looks
highly suspicious, but also predicts the likelihood of whether the region could
undergo malignant transformation, leading to full-blown lung cancer.
The deep learning model was developed by scientists at
Google, while the complex and highly customized software was engineered by
The salient features of the study procedure are
briefly highlighted below:
"Most of the
software we use as clinicians is designed for patient care, not for research,"
- 6,716 de-identified LDCT scans were
used for the analysis
- The LDCT scans were obtained from
Northwestern Medicine's Electronic Data Warehouse
- The LDCT scans enabled validation of
the accuracy of the deep learning system
- The AI-powered technology detected
even very tiny malignant lung nodules
says Etemadi. "It took over a year of dedicated effort by
my entire team to extract and prepare data to help with this exciting project.
The ability to collaborate with world-class scientists at Google, using their
unprecedented computing capabilities to create something with the potential to
save tens of thousands of lives a year is truly a privilege."
The major findings are highlighted below:
"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,"
deep learning technology exhibited high specificity and sensitivity for
detecting lung cancer upon first-time screening
deep learning technology produced lesser numbers of false positives and
false positives or negatives mean better diagnostic efficacy and fewer
deep learning technology outperformed six radiologists when prior LDCT
scans were available and were as good as the radiologists when the prior
scans were unavailable
technology enables doctors to emphatically say whether a patient has lung
cancer or not
technology prevents unnecessary invasive follow-up procedures such as lung
biopsies, which are not only risky, but also costly
"AI in 3D can be much more sensitive in its ability to detect early
lung cancer than the human eye looking at 2D 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."
Lung Cancer: Facts & Figures
- Lung cancer is the most common cause of
cancer-related death in the US
- 160,000 lung cancer
deaths occurred in 2018 in the US
- Chest screening can
detect lung cancer and reduce mortality rates, as per clinical trials in
the US and UK
- Limited access and
high error rates of chest screening results in lung cancer detection at
- Treatment of lung
cancer is much more difficult at advanced stages
Although AI-based technology
still needs to be evaluated in large-scale clinical trials, it nevertheless has
the potential to significantly improve the management and outcome of lung
"This area of
research is incredibly important, as lung cancer has the highest rate of
mortality among all cancers, and there are many challenges in the way of broad
adoption of lung cancer screening,"
says Shetty. "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."
- End-to-End Lung Cancer Screening with Three-dimensional Deep Learning on Low-dose Chest Computed Tomography - (https://aitopics.org/doc/news:86DCA4FF/)