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Rice-Sized Device Tests Brain Tumor’s Drug Responses During Surgery

Posted on by Lawrence Tabak, D.D.S., Ph.D.

Determining most effective tumor-specific drug. A transparent head with a brain tumor. A zoomed in version show a small cylinder with 10 tiny holes embedded in the tumor. Each hole has a different drug leaking out.
A device implanted into a tumor during surgery delivers tiny doses of up to 20 drugs to determine each treatment’s effects. Credit: Donny Bliss, NIH

Scientists have made remarkable progress in understanding the underlying changes that make cancer grow and have applied this knowledge to develop and guide targeted treatment approaches to vastly improve outcomes for people with many cancer types. And yet treatment progress for people with brain tumors known as gliomas—including the most aggressive glioblastomas—has remained slow. One reason is that doctors lack tests that reliably predict which among many therapeutic options will work best for a given tumor.

Now an NIH-funded team has developed a miniature device with the potential to change this for the approximately 25,000 people diagnosed with brain cancers in the U.S. each year [1]. When implanted into cancerous brain tissue during surgery, the rice-sized drug-releasing device can simultaneously conduct experiments to measure a tumor’s response to more than a dozen drugs or drug combinations. What’s more, a small clinical trial reported in Science Translational Medicine offers the first evidence in people with gliomas that these devices can safely offer unprecedented insight into tumor-specific drug responses [2].

These latest findings come from a Brigham and Women’s Hospital, Boston, team led by Pierpaolo Peruzzi and Oliver Jonas. They recognized that drug-screening studies conducted in cells or tissue samples in the lab too often failed to match what happens in people with gliomas undergoing cancer treatment. Wide variation within individual brain tumors also makes it hard to predict a tumor’s likely response to various treatment options.  

It led them to an intriguing idea: Why not test various therapeutic options in each patient’s tumor? To do it, they developed a device, about six millimeters long, that can be inserted into a brain tumor during surgery to deliver tiny doses of up to 20 drugs. Doctors can then remove and examine the drug-exposed cancerous tissue in the laboratory to determine each treatment’s effects. The data can then be used to guide subsequent treatment decisions, according to the researchers.

In the current study, the researchers tested their device on six study volunteers undergoing brain surgery to remove a glioma tumor. For each volunteer, the device was implanted into the tumor and remained in place for about two to three hours while surgeons worked to remove most of the tumor. Next, the device was taken out along with the last piece of a tumor at the end of the surgery for further study of drug responses.

Importantly, none of the study participants experienced any adverse effects from the device. Using the devices, the researchers collected valuable data, including how a tumor’s response changed with varying drug concentrations or how each treatment led to molecular changes in the cancerous cells.

More research is needed to better understand how use of such a device might change treatment and patient outcomes in the longer term. The researchers note that it would take more than a couple of hours to determine how treatments produce less immediate changes, such as immune responses. As such, they’re now conducting a follow-up trial to test a possible two-stage procedure, in which their device is inserted first using minimally invasive surgery 72 hours prior to a planned surgery, allowing longer exposure of tumor tissue to drugs prior to a tumor’s surgical removal.

Many questions remain as they continue to optimize this approach. However, it’s clear that such a device gives new meaning to personalized cancer treatment, with great potential to improve outcomes for people living with hard-to-treat gliomas.


[1] National Cancer Institute Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Brain and Other Nervous System Cancer.

[2] Peruzzi P et al. Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas. Science Translational Medicine DOI: 10.1126/scitranslmed.adi0069 (2023).


Brain Tumors – Patient Version (National Cancer Institute/NIH)

Pierpaolo Peruzzi (Brigham and Women’s Hospital, Boston, MA)

Jonas Lab (Brigham and Women’s Hospital, Boston, MA)

NIH Support: National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Neurological Disorders and Stroke

Artificial Intelligence Speeds Brain Tumor Diagnosis

Posted on by Dr. Francis Collins

Real time diagnostics in the operating room
Caption: Artificial intelligence speeds diagnosis of brain tumors. Top, doctor reviews digitized tumor specimen in operating room; left, the AI program predicts diagnosis; right, surgeons review results in near real-time.
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.


[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.


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

Creative Minds: A New Way to Look at Cancer

Posted on by Dr. Francis Collins

Bradley Bernstein

Bradley Bernstein

Inside our cells, strands of DNA wrap around spool-like histone proteins to form a DNA-histone complex called chromatin. Bradley Bernstein, a pathologist at Massachusetts General Hospital, Harvard University, and Broad Institute, has always been fascinated by this process. What interests him is the fact that an approximately 6-foot-long strand of DNA can be folded and packed into orderly chromatin structures inside a cell nucleus that’s just 0.0002 inch wide.

Bernstein’s fascination with DNA packaging led to the recent major discovery that, when chromatin misfolds in brain cells, it can activate a gene associated with the cancer glioma [1]. This suggested a new cancer-causing mechanism that does not require specific DNA mutations. Now, with a 2016 NIH Director’s Pioneer Award, Bernstein is taking a closer look at how misfolded and unstable chromatin can drive tumor formation, and what that means for treating cancer.