CAD: Striving for Better Lung Ca Imaging Reports


Swiss researchers aim to make radiology reports more meaningful by extracting TNM staging data

Computer-aided detection (CAD) has made its mark in the cancer field, most notably for breast cancer detection. In lung cancer, the use of CAD systems can improve the performance of radiologists in pinpointing pulmonary nodules. But CAD systems in lung cancer have suffered from some shortcomings, such as only little to modest improvement in sensitivity, an increase in false positives, and issues with the level of automation as well as the ability to detect different types and shapes of nodules.

Another potential pitfall: How can CAD results, which are rendered in the language of radiologists, be matched with the language of oncologists?

Bram Stieltjes, MD, PhD, of the University Hospital Basel in Switzerland, and colleagues sought to answer that question by developing an in-house CAD image-processing software for PET/CT lung studies. The goal with a study they presented at the 2017 Radiological Society of North America (RSNA) meeting in Chicago was to decrease tumor, node, distant metastasis (TNM) misstaging, and subsequent erroneous treatment planning for lung cancer patients.

Stieltjes spoke with ASCO Reading Room about the impetus for this research, and the plans for launching the CAD program at his institution as soon as early 2018.

Study Details

The group evaluated reports from the radiology information system (RIS) of 145 non-small cell lung cancer (NSCLC) patients who underwent a primary staging FDG-PET/CT exam at the facility. TNM (edition 7) was determined according to the text information in the reports by a radiologist and a nuclear medicine physician.

The team then downloaded the corresponding PET and CT image data sets from the university’s picture archiving and communication system (PACS). These data sets were transferred to 3D-slicer-based prototype software. As the authors explained, the image-processing application allows for manual segmentation of tumors, lymph nodes, and metastasis using a set of labels including location information and morphological TNM features.

Stieltjes et al reported that in a substantial number of patients, not enough information was provided by the original report to extract a distinct TNM stage:

  • T: 18.6% (27/145)
  • N: 10.3% (15/145)
  • M: 2.1% (3/145)

“Furthermore, in 29 cases, there was a considerable discrepancy between the report and annotation: upstaging due to the annotations: T: n=11, N: n=6, M: n=4; downstaging due to the annotations: T: n=3; N: n=4; M: n=1.”

Applying the image-processing tool and using a segmentation-based approach to the image data sets allowed the team to extract TNM information in all patients, the researchers explained, adding that their approach with tumor labels allows for a clear definition of cancerous lesions in a standardized and reproducible manner.

“We could demonstrate that the proper TNM stage could not be derived from unstructured PET/CT reports in a roughly 30% of the cases. This commonly affects the T-stage because of missing diameter measurements, but also the N and M stage.”

The investigators concluded that the labels generated using this image-processing tool could be directly translated into clinical decision-making, such as tumor boards, and were less prone to interpretation.

The following interview with Stieltjes, who is the head of research coordination for radiologist and nuclear medicine at the Basel institution, has been edited for length and clarity.

Why did your group decide to undertake this study?

Stieltjes: We were trying to standardize our output, and perhaps implement the much-hyped machine learning — what some might call a type of artificial intelligence inroutine clinical practice. I’ve been [at Basel] about 3 years, and our first year we looked for projects that might be suitable to achieve this goal. Lung tumor staging was one such project where the data was there, and there was a substantial number of patients.

The clinical work time that is done on preparing a lung PET/CT report is very high; we [radiologists] take about an hour per report. So there is much that can be gained in terms of efficiency. My gut feeling was that … even when we put in the work on interpretation and reporting, we don’t always deliver the information that is necessary for the oncologists.

There’s an ongoing conversation in radiology about the usefulness of structured reporting versus the free-text reporting described in your study. Do you see structured reporting being of some benefit along with the image-processing tool used in your research?

Stieltjes: Structured reporting does not touch on the radiological workflow as it is right now; it is only formalizing the way text is inputted. Radiology is still interpreting the image and generating a report. However the link between the text and the image is very weak.

What we are presenting is a sort of third way: You first define all the important anatomical features in a structured way with labels. Then the job of the radiologist is not scrolling and talking, but scrolling and clicking, labeling — to place a label on an image in the study series. This then goes to a database.

As a recipient of the report, I can learn and understand what the radiologist sees; I’m able to have a direct link between his or her knowledge and the actual place in the image where their skills have seen an area of importance.

Is the system described in the study ready for clinical deployment?

Stieltjes: We’re on the brink of putting the annotation portion for lung tumors into clinical routine — not the decision-making part. What we’re trying to do is change the radiologist workflow, so that they’re labeling as part of their report — so we focused on getting the annotation part done first. We hope to have it into practice in the early part of 2018.

The next part is the capability to detect all lung lesions, which the radiologist will review, and we’re confident that we’ll have that in the next generation of the application.

What feedback have you received from oncologists and other clinical colleagues about this system?

Stieltjes: This project has been conducted in partnership with our oncology department. The oncologists are really looking forward to having all the TNM information in our reports for two reasons: One, TNM information was not always included in reports; and two, images were not always included in reports. Now labeled images and TNM stage will be part of every report.

Of course, oncologists want to have pathology for correlation, so our TNM stage will be provisional until they get a pathological confirmation.

Should We Set a Higher Bar for Coronary Angiography?


Compared with Ontario, obstructive disease is less common in patients who undergo catheterization in New York.

A recent study indicated that more cardiac catheterizations are performed per capita in New York State than in Ontario, Canada). Now, the same investigators have compared the prevalence of obstructive coronary artery disease (CAD) — defined as diameter stenosis ≥50% in the left main or ≥70% in a major epicardial vessel — in patients undergoing the procedure in the two regions.

Obstructive CAD was found in significantly more of approximately 55,000 patients undergoing a first elective cardiac catheterization during 2008–2011 in Ontario than in some 18,000 such patients in New York (45% and 30%, respectively). Compared with the Canadian patients, the New Yorkers were younger and more likely to be women or to have no or atypical symptoms; the prevalence of several other risk factors also differed significantly between the two groups. Fewer patients in New York than in Ontario had noninvasive evaluations (63% vs. 75%, P<0.001), and the predicted preprocedure probability of obstructive CAD was significantly lower in New York.

Among patients with obstructive CAD, those in New York were significantly more likely than those in Ontario to undergo revascularization (percutaneous coronary intervention, 55% vs. 35%; coronary artery bypass grafting, 20% vs. 14%). Higher crude 30-day mortality in New York than in Ontario was mainly attributable to higher mortality in patients without obstructive CAD.

COMMENT

These findings suggest that the relatively high cardiac catheterization rate in New York results primarily from selecting patients at lower predicted probabilities of obstructive coronary artery disease. The investigators could not control for regional differences in patient, societal, and physician characteristics, preferences, and expectations; nor could they assess which catheterization rate is more appropriate. Nonetheless, the higher prevalence of interventionalists and cardiac invasive capabilities — as well as market-oriented financing — in New York seems likely to account for much, if not all, of the disparity; if so, these data illuminate an opportunity to reduce unnecessary healthcare expenditures.

Source: NEJM

FDA approves first drug-eluting stent for treatment of CAD in patients with diabetes.


Medtronic has announced FDA approval of the Resolute Integrity drug-eluting stent for the treatment of coronary artery disease, which is also the first stent approved for coronary artery disease patients with diabetes.

The agency’s approval was based on results from the global RESOLUTE clinical program, consisting of a large randomized controlled trial and a series of confirmatory single-arm studies involving nearly 250 sites in 32 countries. The program enrolled more than 5,100 patients who received the drug-eluting stent — about one-third of whom had diabetes.

The RESOLUTE US program enrolled 1,402 patients (34% with diabetes) from 128 US-based clinical trial sites. At 1-year follow-up, researchers found low rates of target lesion failure (4.7%), clinically driven target lesion revascularization (2.8%) and definite/probable stent thrombosis (0.1%). A prespecified analysis of patients with diabetes who received the Resolute drug-eluting stent at 1 year also showed low rates of target lesion failure (6.6%), target lesion revascularization (3.4%) and definite/probable stent thrombosis (0.3%). Further, the Resolute drug-eluting stent matched the safety and efficacy of the Xience V drug-eluting stent (Abbott) in two separate large randomized controlled trials.

Additionally, 1- and 2-year data from the Resolute All-Comers study, which enrolled nearly 2,300 patients at 17 centers, were published in The New England Journal of Medicine and The Lancet, respectively.

Similar to Medtronic’s Integrity bare metal stent, the Resolute Integrity drug-eluting stent uses continuous sinusoid technology, which encompasses one continuous single strand of wire that is molded into a sinusoidal wave and then wrapped in a helical pattern and laser-fused at certain points.

“The Resolute Integrity drug-eluting stent offers several notable benefits, starting with outstanding deliverability, which means it’s exceptionally easy to navigate the stent on the delivery system through the coronary vasculature to the narrowed arterial segment that requires treatment,” Martin B. Leon, MD, director of the center for interventional vascular therapy at New York-Presbyterian/Columbia University Medical Center and principal investigator of the RESOLUTE US clinical study, said in a press release. “Its approval by the FDA is based on the impressive performance of the Resolute drug-eluting stent in a wide variety of patients. With the device’s compelling combination of deliverability, efficacy and safety, not to mention that it is the first drug-eluting stent approved for patients with diabetes, the Resolute Integrity drug-eluting stent promises to gain rapid acceptance in cath labs nationwide.”

Source: Endocrine Today.Cardiology.