Evidence map›Paper›PMID 33937842›Full record

ArticleRadiology. Artificial intelligence2020

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma.

Amy J Weisman, Minnie W Kieler, Scott B Perlman, Martin Hutchings, Robert Jeraj, Lale Kostakoglu, Tyler J Bradshaw

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  16. Automated Lugano Metabolic Response Assessment inJournal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024
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  19. Deep learning for [The Lancet. Digital health · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amy J WeismanDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).ORCID 0000-0001-5230-7782
Minnie W KielerDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).
Scott B PerlmanDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).ORCID 0000-0001-9744-7105
Martin HutchingsDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).
Robert JerajDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).
Lale KostakogluDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).
Tyler J BradshawDepartment of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).ORCID 0000-0001-9549-7002

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo automatically detect lymph nodes involved in lymphoma on fluorine 18 ( MATERIALS AND

methodsIn this retrospective study, baseline disease of 90 patients with lymphoma was segmented on

resultsAcross all 90 patients, a range of 0-61 nodes per patient was detected. At an average of four FP findings per patient, the method achieved a TPR of 85% (923 of 1087 nodes). Performance varied widely across patients (TPR range, 33%-100%; FP range, 0-21 findings). In the 20 patients labeled by both physicians, a range of 1-49 nodes per patient was detected and labeled. The second reader identified 96% (210 of 219) of nodes with an additional 3.7 per patient compared with the first reader. In the same 20 patients, the CNN achieved a 90% (197 of 219) TPR at 3.7 FP findings per patient.

conclusionAn ensemble of three-dimensional CNNs detected lymph nodes at a performance nearly comparable to differences between two physicians' annotations. This preliminary study is a first step toward automated PET/CT assessment for lymphoma.© RSNA, 2020.

Identifiers

PMID33937842
PMCPMC8082306

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.