What Is the JNET Classification for Polyps?

The JNET classification is a four-category visual grading system that helps doctors predict what a colorectal polyp is made of while they are still looking at it through the colonoscope. Developed by the Japan NBI Expert Team, it sorts polyps into Types 1, 2A, 2B, and 3 based on their blood vessel patterns and surface textures under magnifying endoscopy with narrow-band imaging (NBI). Each type corresponds to a likely tissue diagnosis, ranging from harmless hyperplastic polyps at one end to deeply invasive cancers at the other. The system’s real purpose is practical: it guides the endoscopist’s decision about whether a polyp can be left alone, removed during the procedure, or needs surgery.

Why the Classification Exists

Before JNET, Japanese endoscopists had at least four competing systems for interpreting what they saw under NBI magnification, including the Sano, Hiroshima, Showa, and Jikei classifications. Each worked reasonably well in the hands of its creators, but the lack of a shared language made it hard to compare results across centers or train new endoscopists consistently. In 2011, a group of Japanese experts formed a working committee to merge these systems into one. They reached consensus using a structured voting process and published the unified JNET classification in 2014.¹ A multicenter validation study followed to confirm the categories held up across different hospitals and operators.²

The classification evaluates two features visible under magnified NBI: the vessel pattern and the surface pattern. Vessel pattern refers to the arrangement of tiny blood vessels on the polyp’s surface. Surface pattern describes the mucosal pits and ridges. Together, these two features create a visual fingerprint that maps onto what a pathologist would find under the microscope.

The Four Types and What They Mean

Each JNET type predicts a different tissue diagnosis. Understanding the categories helps make sense of colonoscopy reports, treatment recommendations, and why certain polyps get handled differently from others.

Type 1: The vessel pattern is invisible or barely visible, and the surface looks similar to the surrounding normal mucosa with regular dark or white spots. This pattern predicts a hyperplastic polyp or a sessile serrated lesion without dysplasia. These are generally benign, and small ones may be left in place or removed with a simple cold snare.³
Type 2A: The vessels become visible as a regular meshwork, and the surface has an orderly tubular or rounded pit pattern. This appearance predicts a low-grade adenoma, the common precancerous polyp. Most of these can be removed endoscopically with standard techniques.
Type 2B: The vessel pattern is irregular, and the surface shows an irregular or obscured pattern. This category predicts high-grade dysplasia, very early cancer limited to the mucosa, or cancer that has just begun to invade the top layer of the submucosa (T1a). These lesions typically need en bloc removal by endoscopic submucosal dissection (ESD) so the pathologist can evaluate invasion depth on an intact specimen.
Type 3: The vessels are disrupted or absent in areas, and the surface pattern is amorphous or completely destroyed. This predicts cancer that has invaded deeply into the submucosa (T1b or deeper), which usually requires surgery rather than endoscopic treatment.⁴

The key clinical dividing line sits between Type 2A and Type 2B. A Type 2A polyp can almost always be handled endoscopically without much worry. Once a polyp crosses into Type 2B territory, the stakes rise because of the possibility of cancer, and the treatment approach shifts toward more careful, en bloc resection or surgical referral.⁵

How Accurate Is It in Practice

A large-scale clinical practice study evaluating over a thousand lesions found that the system performs well at the extremes but struggles in the middle. For Type 1, the accuracy was about 93%, with strong specificity (96%) meaning false alarms were rare. Type 2A had 87% accuracy and high sensitivity (91%), so most adenomas were correctly identified. Type 3 had 98% accuracy with perfect specificity, meaning when an endoscopist called something Type 3, it was essentially always deeply invasive cancer.⁶

Type 2B, however, is the weak link. In that same study, its sensitivity was only 42%, meaning the system missed more than half the lesions that actually belonged in this category. The positive predictive value was just 26%, so roughly three out of four polyps labeled 2B turned out to be something else, usually a low-grade adenoma that looked deceptively irregular. A randomized trial comparing NBI to other imaging technologies reported somewhat better numbers for Type 2B, with accuracy around 79%, but the pattern is consistent across studies: Type 2B is where the classification wobbles.⁷

The Type 2B Problem

The original intent behind splitting the old “Type 2” into 2A and 2B was to distinguish ordinary adenomas from early cancers before making a treatment decision. In theory, identifying a polyp as 2B would tell the endoscopist that en bloc removal by ESD was needed, while a 2A polyp could be removed more simply. In practice, the boundary between 2A and 2B is blurry, and Type 2B remains the biggest challenge for endoscopists.⁸

Part of the difficulty is that Type 2B encompasses a wide spectrum of actual tissue types. One study using blue laser imaging found that among lesions classified as 2B, roughly 19–39% turned out to be merely low-grade dysplasia on pathology, while 8–12% were deeply invasive cancers that really should have been classified as Type 3.⁹ A separate study from a North American center reported that JNET 2B had about 80% accuracy for predicting high-grade dysplasia or shallow submucosal invasion, which is considerably better than some Japanese cohorts reported, but still leaves one in five lesions misclassified.¹⁰

To deal with this ambiguity, many centers add magnifying chromoendoscopy (spraying a dye on the polyp surface) whenever a lesion lands in the 2B category. One study found that among JNET Type 2B lesions, about 70% were high-grade dysplasia or shallow cancers, but roughly 13% turned out to be deeply invasive cancers that needed surgery.¹¹ Adding dye-based pit pattern analysis helps refine the prediction and sort out which 2B lesions are safe for ESD and which should be sent to a surgeon.

Sessile Serrated Lesions and Their Quirks

Sessile serrated lesions (SSLs) are a particular subtype of polyp that the JNET system groups under Type 1 alongside ordinary hyperplastic polyps. That grouping makes sense visually because SSLs often look similar to hyperplastic polyps under NBI. The problem is that SSLs, unlike hyperplastic polyps, can progress to cancer through a different molecular pathway and genuinely need to be removed, especially larger ones or those with dysplasia.

A multicenter prospective study testing magnifying colonoscopy for detecting SSLs found sensitivity of about 80% overall, which dropped only slightly to 82% even when endoscopists felt confident in their diagnosis.¹² That means roughly one in five SSLs was missed or misidentified, which is not ideal for a lesion type that requires removal. The flat, pale appearance of SSLs makes them tricky even with magnification. In clinical practice, endoscopists often rely on non-magnified clues like a mucus cap, indistinct borders, and a cloudy surface to spot SSLs before zooming in for JNET classification.

How Much Training Does It Take

The JNET classification is designed to be learnable, but experience matters enormously. A study measuring the effect of a structured educational lecture found that endoscopists with limited experience improved their correct diagnosis rate from about 60% to 68% after training, while those with more background jumped from 66% to 87%. Interobserver agreement, the measure of how often two doctors looking at the same polyp reach the same conclusion, also improved with training. Among the more experienced group, the kappa value (a measure of agreement beyond chance) rose from 0.39 to 0.75 after the lecture.¹³

Even among trained endoscopists, though, agreement is far from perfect. A prospective study found that inter-observer agreement for the JNET classification applied to NBI reached a kappa of about 0.61, which statisticians call “substantial” but which in practical terms means doctors disagree on the category about a third of the time.¹⁴ The disagreement concentrates, unsurprisingly, around Type 2B, where the judgment call about whether a vessel or surface pattern is “irregular” versus merely “slightly disorganized” is inherently subjective. A web-based diagnostic concordance study from a tertiary oncology center confirmed that assessment of irregularity in the JNET system is subjective, particularly for borderline lesions being evaluated for ESD versus surgery.¹⁵

Performance Outside Japan

Most JNET validation work has come from Japanese centers where magnifying colonoscopy is routine and endoscopists train with it from the start of their careers. The picture changes when you look at how well the system works in the hands of European endoscopists who may have less familiarity with magnification. An international multicenter web-based trial involving European Society of Gastrointestinal Endoscopy (ESGE) members found noticeably lower performance. Among ESGE participants, the sensitivity and accuracy for Type 2A dropped to 53% and 62%, respectively, and for Type 2B to 44% and 55%. Even Type 1 sensitivity was lower than in Japanese cohorts, at 73%. When Types 2B and 3 were combined into a single “cancer” category, the sensitivity for picking up high-grade dysplasia and cancer was still only about 60%.¹⁶

These numbers are sobering but not entirely unexpected. Magnifying endoscopy with NBI is standard equipment in Japan but far less common in Western endoscopy units. Many European and North American colonoscopists use non-magnifying NBI or other imaging enhancement technologies. The gap in performance highlights that the JNET system is not just a chart you memorize; it requires hands-on experience with magnification-level detail that some practice settings simply do not offer routinely.

Can It Be Used with Other Imaging Technologies

NBI is the imaging mode the JNET classification was built for, but it is not the only enhanced imaging technology available. Blue laser imaging (BLI) and its LED-based variant (LED-BLI), manufactured by a different endoscope maker, produce similar contrast enhancement. A randomized controlled trial directly comparing NBI, laser-BLI, and LED-BLI found that both BLI technologies were statistically non-inferior to NBI across all JNET categories.⁷ A separate comparison found that laser-BLI and LED-BLI agreed with each other on JNET category about 93% of the time, with an excellent weighted kappa of 0.99, regardless of the endoscopist’s experience level or confidence.¹⁷

This cross-platform compatibility is meaningful because it means endoscopy units equipped with different manufacturers’ scopes can still apply the same JNET framework. The surface and vessel patterns that define each JNET type appear similarly enough under BLI and LED-BLI that the diagnostic criteria translate without major modification.

Artificial Intelligence and JNET

Computer-aided diagnosis (CADx) systems trained on the JNET classification are an active area of development, partly because they could help bridge the experience gap between expert and novice endoscopists. One CADx system achieved 92% accuracy in classifying polyps by JNET type, outperforming beginners (70%), intermediate endoscopists (83%), and even experts (79%). Its negative predictive value for advanced neoplasia was 100%, meaning it never missed a cancer in the test set.¹⁸

A separate CADx system developed for multi-class JNET diagnosis reported 97% accuracy for Type 1 and 86% for Type 2A, but the familiar trouble spot emerged again: Type 2B accuracy was 84%, with a positive predictive value of only 47%, meaning more than half the polyps the computer flagged as 2B were actually something else.¹⁹ The AI struggles with the same boundary that trips up human endoscopists. Still, these systems show genuine promise as a real-time second opinion, especially in settings where magnifying endoscopy expertise is scarce.

How JNET Guides Treatment Decisions

The practical value of the JNET classification lies in steering what happens to the polyp once it is found. A Type 1 lesion that looks like a straightforward hyperplastic polyp in the rectosigmoid area may not even need removal, saving the patient from an unnecessary procedure. Type 2A adenomas are removed using standard polypectomy or endoscopic mucosal resection, depending on size. Type 2B lesions prompt en bloc resection by ESD so the pathologist can examine the full specimen for invasion depth. Type 3 lesions, with their disrupted architecture suggesting deep invasion, are referred for surgery.²⁰

The “resect and discard” strategy, which has gained traction as a way to reduce pathology costs, also relies on classifications like JNET. The idea is that if the endoscopist can confidently identify a small polyp as Type 1 or Type 2A during the procedure, the polyp can be removed and discarded without sending it for formal histopathology. The high negative predictive values for advanced neoplasia in both human and AI-assisted JNET assessments support this approach for small, clearly benign-looking polyps. The strategy does not apply to anything that looks remotely like Type 2B or 3, where tissue analysis is critical.

What Shows Up on Your Colonoscopy Report

If your colonoscopy was performed at a center that uses magnifying NBI, you might see JNET types mentioned in the procedure report. A note like “JNET Type 2A” next to a polyp description tells you the endoscopist assessed it as a likely low-grade adenoma based on its visual features. These are the bread-and-butter polyps found during routine colonoscopy, and they are removed as a matter of course.

If the report mentions “JNET Type 2B,” the endoscopist saw features suggesting possible early cancer or high-grade precancerous change. This does not mean you have cancer confirmed. It means the visual appearance warranted more careful removal and pathologic examination. The final diagnosis comes from the pathologist looking at the tissue under a microscope, not from the endoscopic classification alone. Given that Type 2B’s positive predictive value for its target diagnoses is modest, many polyps labeled 2B on endoscopy turn out to be ordinary adenomas on pathology. The classification is designed to err on the side of caution, prompting more thorough handling of anything that looks suspicious rather than risking under-treatment.

Not every colonoscopy report will use JNET terminology. Many Western centers use the simpler NICE classification (which has only three categories and does not require magnification), or describe polyps in more general terms. The JNET system’s dependence on high-quality magnifying endoscopes and trained operators limits its adoption outside specialized centers, particularly outside Japan and parts of East Asia.