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.
 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.
Experiencing a range of emotions is a normal part of human life, but much remains to be discovered about the neuroscience of mood. In a step toward unraveling some of those biological mysteries, researchers recently identified a distinctive pattern of brain activity associated with worsening mood, particularly among people who tend to be anxious.
In the new study, researchers studied 21 people who were hospitalized as part of preparation for epilepsy surgery, and took continuous recordings of the brain’s electrical activity for seven to 10 days. During that same period, the volunteers also kept track of their moods. In 13 of the participants, low mood turned out to be associated with stronger activity in a “subnetwork” that involved crosstalk between the brain’s amygdala, which mediates fear and other emotions, and the hippocampus, which aids in memory.
For obese people with diabetes, doctors have increasingly been offering gastric bypass surgery as a way to lose weight and control blood glucose levels. Short-term results are often impressive, but questions have remained about the long-term benefits of such operations. Now, a large, international study has some answers.
Soon after gastric bypass surgery, about 50 percent of folks not only lost weight but they also showed well-controlled blood glucose, cholesterol, and blood pressure. The good news is that five years later about half of those who originally showed those broad benefits of surgery maintained that healthy profile. The not-so-good news is that the other half, while they generally continued to sustain weight loss and better glucose control, began to show signs of increasing risk for cardiovascular complications.
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.
An increasing number of women with cancer in one breast are choosing to have both breasts surgically removed in hopes of reducing the chance of developing cancer in the unaffected breast. But does this approach—called bilateral, or double, mastectomy—really improve the odds of survival? A new NIH-funded study indicates that, for the vast majority of women, it does not .
A research team led by Allison Kurian, an oncologist at Stanford University School of Medicine, and Scarlett Gomez, an epidemiologist at the Cancer Prevention Institute of California in Fremont, used the California Cancer Registry to study the 10-year survival outcomes of patients diagnosed with early-stage cancer (stages 0–III) in one breast, between 1998 and 2011.