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Prostate Cancer: Combined Biopsy Strategy Makes for More Accurate Diagnosis

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Doctor consult
Credit: iStock

Last year, nearly 175,000 American men were diagnosed with prostate cancer [1]. Most got the bad news after a blood test or physical exam raised concerns that warranted a biopsy of the prostate, a walnut-sized gland just below the bladder.

Traditional biopsies sample tissue from 12 systematically placed points within the prostate that are blind to tumor locations. Such procedures have helped to save many lives, but are prone to missing or misclassifying prostate cancers, which has led doctors both to over and under treat their patients.

Now, there may be a better approach. In a study of more than 2,000 men, NIH researchers and their colleagues recently found that combining the 12-point biopsy with magnetic resonance imaging (MRI)-targeted biopsy during the same session more accurately diagnoses prostate cancer than either technique alone [2].

The findings address a long-standing challenge in prostate cancer diagnostics: performing a thorough prostate biopsy to allow a pathologist to characterize as accurately as possible the behavior of a tumor. Some prostate tumors are small, slow growing, and can be monitored closely without treatment. Other tumors are aggressive and can grow rapidly, requiring immediate intervention with hormonal therapy, radiation, or surgery.

But performing a thorough prostate biopsy can run into technical difficulties. The 12-point biopsy blindly samples tissue from across the prostate gland, but it can miss a cancer by not probing in the right places.

Several years ago, researchers at the NIH Clinical Center, Bethesda, MD, envisioned a solution. They’d use specially designed MRI images of a man’s prostate to guide the biopsy needle to areas in the prostate that look suspicious and deserve a closer look under a microscope.

Through a cooperative research-and-development agreement, NIH and the now- Florida-based Philips Healthcare created an office-based, outpatient prostate biopsy device, called UroNav, that was later approved by the Food and Drug Administration. The UroNav system relies on software that overlays MRI images highlighting suspicious areas onto real-time ultrasound images of the prostate that are traditionally used to guide biopsy procedures.

The new technology led to a large clinical study led by Peter Pinto, a researcher with NIH’s National Cancer Institute. The study results, published in 2015, found that the MRI-targeted approach was indeed superior to the 12-point biopsy at detecting aggressive prostate cancers [3].

But some doctors had questions about how best to implement the UroNav system and whether it could replace the 12-point biopsy. The uncertainty led to a second clinical study to nail down more answers, and the results were just published in The New England Journal of Medicine.

The research team enrolled 2,103 men who had visible prostate abnormalities on an MRI. Once in the study, each man underwent both the 12-point blind biopsy and the MRI-targeted approach—all in a single office visit. Based on this two-step approach, 1,312 people were diagnosed with prostate cancer. Of that total, 404 men had evidence of aggressive cancer, and had their prostates surgically removed.

The researchers then compared the diagnoses from each approach alone versus the two combined. The data showed that the combined biopsy found 208 cancers that the standard 12-point biopsy alone would have missed. Adding the MRI-targeted biopsy also helped doctors find and sample the more aggressive cancers. This allowed them to upgrade the diagnosis of 458 cancers to aggressive and in need of more full treatment.

Combining the two approaches also led to more accurate diagnoses. By carefully analyzing the 404 removed prostates and comparing them to the biopsy results, the researchers found the 12-point biopsy missed the most aggressive cancers about 40 percent of the time. But the MRI-targeted approach alone missed it about 30 percent of the time. Combined, they did much better, underestimating the severity of less than 15 percent of the cancers.

Even better, the combined biopsy missed only 3.5 percent of the most aggressive tumors. That’s compared to misses of about 17 percent for the most-aggressive cancers for the 12-point biopsy alone and about 9 percent for MRI-targeted biopsy alone.

It may take time for doctors to change how they detect prostate cancer in their practices. But the findings show that combining both approaches will significantly improve the accuracy of diagnosing prostate cancer. This will, in turn, help to reduce risk of suboptimal treatment (too much or too little) by allowing doctors and patients to feel more confident in the biopsy results. That should come as good news now and in the future for the families and friends of men who will need an accurate prostate biopsy to make informed treatment decisions.

References:

[1] Cancer State Facts: Prostate Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.

[2] MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. Ahdoot M, Wilbur AR, Reese SE, Lebastchi AH, Mehralivand S, Gomella PT, Bloom J, Gurram S, Siddiqui M, Pinsky P, Parnes H, Linehan WM, Merino M, Choyke PL, Shih JH, Turkbey B, Wood BJ, Pinto PA. N Engl J Med. 2020 Mar 5;382(10):917-928.

[3] Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. Siddiqui M, Rais-Bahrami, George AK, Rothwax J, Shakir N, Okoro C, Raskolnikov D, Parnes HL, Linehan WM, Merino MJ, Simon RM, Choyke PL, Wood BJ, and Pinto PA. JAMA. 2015 January 27;313(4):390-397.

Links:

Prostate Cancer (National Cancer Institute/NIH)

Video: MRI-Targeted Prostate Biopsy (YouTube)

Pinto Lab (National Cancer Institute/NIH)

NIH Support: National Cancer Institute; NIH Clinical Center


Artificial Intelligence Speeds Brain Tumor Diagnosis

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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.

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

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Liquid Biopsy Schematic

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

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UW light sheet microscope team

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

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Dr. Plon with a patient and her family

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.