cancer diagnostics
Artificial Intelligence Speeds Brain Tumor Diagnosis
Posted on by Dr. Francis Collins
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor
Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.
This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.
Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.
As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.
The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.
Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.
To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.
In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.
The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.
Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.
Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.
Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.
The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.
Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.
Reference:
[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.
Links:
Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)
New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)
Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)
Todd Hollon (University of Michigan, Ann Arbor)
NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering
New ‘Liquid Biopsy’ Shows Early Promise in Detecting Cancer
Posted on by Dr. Francis Collins
Caption: Liquid biopsy. Tumor cells shed protein and DNA into bloodstream for laboratory analysis and early cancer detection.
Early detection usually offers the best chance to beat cancer. Unfortunately, many tumors aren’t caught until they’ve grown relatively large and spread to other parts of the body. That’s why researchers have worked so tirelessly to develop new and more effective ways of screening for cancer as early as possible. One innovative approach, called “liquid biopsy,” screens for specific molecules that tumors release into the bloodstream.
Recently, an NIH-funded research team reported some encouraging results using a “universal” liquid biopsy called CancerSEEK [1]. By analyzing samples of a person’s blood for eight proteins and segments of 16 genes, CancerSEEK was able to detect most cases of eight different kinds of cancer, including some highly lethal forms—such as pancreatic, ovarian, and liver—that currently lack screening tests.
In a study of 1,005 people known to have one of eight early-stage tumor types, CancerSEEK detected the cancer in blood about 70 percent of the time, which is among the best performances to date for a blood test. Importantly, when CancerSEEK was performed on 812 healthy people without cancer, the test rarely delivered a false-positive result. The test can also be run relatively cheaply, at an estimated cost of less than $500.
Tumor Scanner Promises Fast 3D Imaging of Biopsies
Posted on by Dr. Francis Collins
Caption: University of Washington team that developed new light-sheet microscope (center) includes (l-r) Jonathan Liu, Adam Glaser, Larry True, Nicholas Reder, and Ye Chen.
Credit: Mark Stone/University of Washington
After surgically removing a tumor from a cancer patient, doctors like to send off some of the tissue for evaluation by a pathologist to get a better idea of whether the margins are cancer free and to guide further treatment decisions. But for technical reasons, completing the pathology report can take days, much to the frustration of patients and their families. Sometimes the results even require an additional surgical procedure.
Now, NIH-funded researchers have developed a groundbreaking new microscope to help perform the pathology in minutes, not days. How’s that possible? The device works like a scanner for tissues, using a thin sheet of light to capture a series of thin cross sections within a tumor specimen without having to section it with a knife, as is done with conventional pathology. The rapidly acquired 2D “optical sections” are processed by a computer that assembles them into a high-resolution 3D image for immediate analysis.
Study Shows DNA Sequencing Brings Greater Precision to Childhood Cancer
Posted on by Dr. Francis Collins
Caption: Baylor’s Sharon Plon consults with a family at the Texas Children’s Cancer Center in Houston.
Credit: Paul V. Kuntz/Texas Children’s Hospital
An impressive number of fundamental advances in our understanding of cancer have occurred over the past several decades. One of the most profound is the realization that cancer is a disease of the genome, driven by a wide array of changes in DNA—some in the germline and affecting all cells of the body, but most occurring in individual cells during life (so-called “somatic mutations”). As the technology for sequencing cancer genomes has advanced, we are learning that virtually all cancers carry a unique set of mutations. Most are DNA copying errors of no significance (we call those “passengers”), but a few of them occur in genes that regulate cell growth and contribute causatively to the cancer (we call those “drivers”). We are now learning that it may be far more important for treating cancer to figure out what driver mutations are present in a patient’s tumor than to identify in which organ it arose. And, as a new study shows, this approach even appears to have potential to help cancer’s littlest victims.
Using genomic technology to analyze both tumor and blood samples from a large number of children who’d been newly diagnosed with cancer, an NIH-funded research team uncovered genetic clues with the potential to refine diagnosis, identify inherited cancer susceptibility, or guide treatment for nearly 40 percent of the children [1]. The potential driver mutations spanned a broad spectrum of genes previously implicated not only in pediatric cancers, but also in adult cancers. While much more work remains to determine how genomic analyses can be used to devise precise, new strategies for treating kids with cancer, the study provides an excellent example of the kind of research that NIH hopes to accelerate under the nation’s new cancer “moonshot,” a research initiative recently announced by the President and being led by the Vice President.
