Discover how Anton Kontunen's groundbreaking research uses diathermy smoke analysis to detect cancer margins during surgery in real-time.
Women diagnosed with breast cancer annually worldwide
Reoperation rates due to incomplete cancer removal
For real-time analysis vs. 30-40 minutes with current methods
In operating rooms worldwide, a silent revolution is brewing—one that sees value where others see waste. Each year, approximately 1.7 million women are diagnosed with breast cancer globally, with many undergoing breast-conserving surgery 1 . In these delicate procedures, surgeons walk a tightrope: remove too much tissue, and they risk disfigurement; remove too little, and cancer may return.
The critical question during surgery is whether all cancerous tissue has been removed—a question that currently takes days to answer through laboratory analysis while patients wait in anxious uncertainty.
Enter Anton Kontunen, a researcher whose pioneering work suggests that the answer to this surgical dilemma might be hiding in plain sight—or more precisely, in the surgical smoke that rises from tissue during cancer operations. This seemingly worthless byproduct of modern surgery may contain precisely the information surgeons need to make critical decisions in the operating room.
In cancer surgery, especially breast-conserving procedures, the single most important factor determining whether cancer will return is whether the surgeon successfully removed all cancerous tissue. The interface between removed tissue and remaining tissue is called the surgical margin, and achieving a "clear margin" (no cancer cells at this boundary) is crucial 1 .
Despite surgical advances, reoperation rates remain startlingly high—up to 30% of breast cancer patients require additional surgery because cancer cells were found at the margins of their removed tissue after the initial procedure 1 .
Traditional approaches to margin assessment have significant limitations:
While accurate, this method requires transporting tissue samples to a pathologist during surgery, adding 30-40 minutes to operation time 1 .
Conventional X-ray imaging struggles with soft tissue sensitivity, making faint lesions or certain cancer types like DCIS difficult to detect 4 .
This technique provides valuable information but suffers from similarity in tissue density between normal and cancerous tissue 4 .
Surgical smoke, often viewed as an unpleasant occupational hazard, is actually a complex chemical cocktail. Generated when electrosurgical instruments heat tissue to the point of vaporization, this smoke contains:
Critically, the smoke contains chemical signatures that differ between healthy and cancerous tissue 6 . Cancer cells have altered metabolism (the Warburg effect) that changes their biochemical composition, particularly their phospholipid profiles 6 .
Early research using Rapid Evaporative Ionization Mass Spectrometry (REIMS) demonstrated that surgical smoke could distinguish malignant from benign tissue with over 95% accuracy 1 . However, mass spectrometry technology is prohibitively expensive and complex for routine surgical use.
This is where Differential Mobility Spectrometry (DMS) enters the picture. Also known as Field Asymmetric Ion Mobility Spectrometry (FAIMS), DMS offers a more practical alternative—a technology that is faster, cheaper, and more robust than mass spectrometry while still capable of detecting the biochemical differences in surgical smoke 1 .
As part of his thesis work at Tampere University, Anton Kontunen developed and validated a complete system for intraoperative cancer margin detection through diathermy smoke analysis 2 . His research followed a logical progression through three key studies:
Development of a specialized filtration system necessary for processing surgical smoke while preserving chemical information.
Establishment of whether healthy tissue identification was possible using the DMS approach.
Demonstration of actual cancer detection capability in human tissue samples.
The system Kontunen developed, called the Automatic Tissue Analysis System (ATAS), combines a DMS sensor with specialized sampling equipment that can collect and analyze smoke generated during electrosurgical procedures 1 .
The surgeon uses a standard electrosurgical blade to cut or coagulate tissue, producing smoke as a byproduct.
A specialized suction device collects the smoke near its generation point. Kontunen's patented filtration system removes particulate matter while allowing the gaseous analytes to pass through to the analysis chamber 2 .
The smoke molecules are ionized, typically using a radioactive or corona discharge ionization source.
Ions are subjected to an asymmetric, alternating electric field. Different ions will have different mobility characteristics in this field, allowing them to be separated based on their size, shape, and charge.
The separated ions hit a detector plate, producing electrical signals that are converted into a 2D dispersion plot—essentially a chemical "fingerprint" of the smoke sample.
Sophisticated algorithms compare the sample's fingerprint against a database of known tissue types, providing the surgeon with nearly instantaneous feedback on whether the tissue being cut is cancerous or not.
| Component | Function | Innovation |
|---|---|---|
| Smoke collection system | Captures surgical smoke at source | Patented filtration device that preserves chemical information |
| DMS sensor | Separates ions based on mobility | Works at atmospheric pressure; faster and cheaper than mass spectrometry |
| Ionization source | Charges molecules for analysis | Corona discharge or radioactive source |
| Machine learning algorithm | Interprets chemical fingerprints | Trained on known tissue samples; improves with use |
In a landmark study published in the European Journal of Surgical Oncology, Kontunen and colleagues demonstrated the impressive capabilities of their DMS-based system 3 . They analyzed:
carcinoma samples from 21 malignant breast tumors
benign samples including normal mammary gland, adipose tissue, and vascular tissue
The system achieved a remarkable 87% classification accuracy when distinguishing malignant from benign tissue, with 80% sensitivity and 90% specificity 3 5 . Perhaps most impressively, the technology could differentiate between ductal and lobular carcinoma types with 94% accuracy for ductal carcinomas 3 .
Kontunen's pilot testing with human brain tumor samples yielded similarly promising results 2 . While detailed data from these experiments requires consultation of the full thesis, the research confirmed that the principle of diathermy smoke analysis applies beyond breast cancer to other tumor types, suggesting potentially broad applicability across surgical specialties.
| Metric | Result | Comparison to Existing Methods |
|---|---|---|
| Overall accuracy | 87% | Superior to specimen radiography (32% sensitivity) 4 |
| Sensitivity | 80% | Comparable to frozen section analysis but faster |
| Specificity | 90% | Reduces false positives that lead to unnecessary tissue removal |
| Time required | Near-real-time | Seconds compared to 30-40 minutes for frozen sections |
| Cost per analysis | Low | Significantly cheaper than mass spectrometry-based approaches |
For those interested in the technical aspects of this groundbreaking work, here are the key components and reagents that made this research possible:
| Item | Function | Specific Example/Note |
|---|---|---|
| Differential Mobility Spectrometer | Separates and detects ions | Custom-built device; commercial DMS systems available |
| Electrosurgical generator | Produces controlled cutting energy | Standard surgical equipment with adjustable settings |
| Smoke evacuation system | Collects and transports smoke | Includes patented filtration 2 |
| Data acquisition software | Captures and processes signals | Custom algorithms for signal processing |
| Machine learning algorithms | Classifies tissue types | Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) |
| Tissue samples | Validation and calibration | Fresh surgical specimens with pathological confirmation |
| Calibration standards | Instrument calibration | Known chemical compounds for system validation |
The most exciting developments in surgical smoke analysis may lie not in merely distinguishing cancerous from non-cancerous tissue, but in identifying specific molecular markers that could guide more personalized treatment approaches. Recent research has focused on identifying specific phospholipids that are elevated in cancer tissue.
Phosphatidylcholine (PC)
Detected with 91% accuracy using SVM classification 6
Phosphatidylinositol (PI)
Identified with 73-74% accuracy 6
Phosphatidylethanolamine (PE)
Detected with 66-72% accuracy 6
This movement from "black box" classification toward specific molecular detection opens possibilities for real-time tissue characterization that goes beyond simple binary classification.
The future likely holds integration of smoke analysis technology with advanced surgical systems:
Systems that can adjust their dissection plane based on real-time smoke analysis feedback
Visual highlighting of potentially cancerous areas based on chemical signatures
Guiding surgeons toward areas with concerning chemical profiles
While Kontunen's work focused on breast and brain tumors, the technology shows promise for many applications:
Anton Kontunen's work on diathermy smoke analysis represents exactly the kind of innovative thinking that moves medical science forward—finding valuable information where others saw only waste. By recognizing that surgical smoke contains meaningful chemical data about the tissue being operated on, and by developing practical technologies to extract that information in real-time, Kontunen and his colleagues have opened a new frontier in cancer surgery.
The implications are profound: surgeons equipped with this technology could potentially know before closing an incision whether they have successfully removed all cancerous tissue. This could dramatically reduce the emotional and physical burden of reoperations on patients, while simultaneously reducing healthcare costs associated with additional procedures.
As this technology continues to develop and validate itself in clinical trials, we may look back on this work as a turning point—the moment when surgery became not just mechanical but analytical, not just about removing tissue but about understanding it at the molecular level in real-time. The invisible intelligence hidden in surgical smoke may soon become one of the surgeon's most valuable allies in the fight against cancer.