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Research ArticleClinical Investigation

aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL Images of Veterans with Prostate Cancer

Nicholas Nickols, Aseem Anand, Kerstin Johnsson, Johan Brynolfsson, Pablo Borreli, Neil Parikh, Jesus Juarez, Lida Jafari, Mattias Eiber and Matthew Rettig
Journal of Nuclear Medicine February 2022, 63 (2) 233-239; DOI: https://doi.org/10.2967/jnumed.120.261863
Nicholas Nickols
1Radiation Oncology Service, VA Greater Los Angeles Healthcare System, Los Angeles, California;
2Department of Radiation Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;
3Department of Urology, David Geffen School of Medicine, UCLA, Los Angeles, California;
4Institute of Urologic Oncology, Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California;
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Aseem Anand
5Department of Translational Medicine, Division of Urological Cancer, Lund University, Malmö, Sweden;
6Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York;
7Department of Data Science and Machine Learning, EXINI Diagnostic AB, Lund, Sweden;
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Kerstin Johnsson
7Department of Data Science and Machine Learning, EXINI Diagnostic AB, Lund, Sweden;
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Johan Brynolfsson
7Department of Data Science and Machine Learning, EXINI Diagnostic AB, Lund, Sweden;
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Pablo Borreli
8Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden;
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Neil Parikh
2Department of Radiation Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;
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Jesus Juarez
2Department of Radiation Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;
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Lida Jafari
9Imaging Service, VA Greater Los Angeles Healthcare System, Los Angeles, California;
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Mattias Eiber
10Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; and
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Matthew Rettig
2Department of Radiation Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;
3Department of Urology, David Geffen School of Medicine, UCLA, Los Angeles, California;
11Division of Hematology-Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, California
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  • FIGURE 1.
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    FIGURE 1.

    Deep-learning–enabled segmentation of anatomic context in low-dose CT component of PET/CT. Individual color represents respective segmented organ. aPROMISE technology enables automated segmentation of reference organs and anatomic delineation of disease in prostate tumor, regional lymph node, and distant metastases.

  • FIGURE 2.
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    FIGURE 2.

    Example of patients who were negative in aPROMISE-assisted reads of 18F-DCFPyL scans (A and C, axial images) compared with Na18F (B and D, axial images) and were downstaged from N0M1 to N0M0.

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    FIGURE 3.

    Quantitative reproducibility of miPSMA index in patients who were categorized the same in 2 independent aPROMISE-assisted reads: miN0M0 (A), miN1M0 (B), and miN0M1(C). In A, 1 patient was excluded because of a manual segmentation error that incorporated bladder. Cor = correlation.

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    FIGURE 4.

    miPSMA index values in prostate, stratified by PSA (A and B) and separately by Gleason grade (C).

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    TABLE 1.

    Patient Characteristics (n = 109)

    CharacteristicData
    Age (y)
     Average70
     Median69
     Minimum55
     Maximum86
    Race (n)
     White54 (49%)
     African American44 (41%)
     Hispanic7 (7%)
     Asian Pacific Islander3 (2%)
     Native American1 (1%)
    Clinical T stage (n)
     cT1/262 (57%)
     cT347 (43%)
    Gleason score (n)
     3 + 313 (7%)
     3 + 428 (23%)
     4 + 324 (18%)
     ≥4 + 449 (36%)
    PSA at diagnosis (ng/mL)
     Average20.4 ng/mL
     Median13.55 ng/mL
     Minimum3.03 ng/mL
     Maximum167.92 ng/mL
    Percentage positive core (n)
     <25%18 (17%)
     25%–50%28 (26%)
     51%–75%14 (13%)
     >75%28 (26%)
     Unknown11 (10%)
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    TABLE 2.

    aPROMISE-PSMA Staging Reads vs. Local and Distant Metastatic Staging by Conventional Imaging

    Conventional imaging
    Read no.ParameterN0M0 (n = 87)N1M0 (n = 8)N0M1a/b (n = 14)N1M1a/b (n = 0)
    1miN0M0 (n = 71)67040
    miN1M0 (n = 19)13600
    miN0 M1a/b (n = 15)6090
    miN1M1a/b (n = 4)1210
    2miN0M0 (n = 72)68040
    miN1M0 (n = 18)12600
    miN0M1a/b (n = 15)6090
    miN1M1a/b (n = 4)1210
    • n = 109.

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    TABLE 3.

    Local and Distant Metastatic Staging by aPROMISE-PSMA Read 1 Against aPROMISE-PSMA Read 2

    Read 1
    Read 2miN0M0 (n = 71)miN1M0 (n = 19)miN0M1a/b (n = 15)miN1M1a/b (n = 4)
    miN0M0 (n = 72)67230
    miN1M0 (n = 18)11700
    miN0M1a/b (n = 15)30120
    miN1M1a/b (n = 4)0004
    • n = 109.

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Journal of Nuclear Medicine: 63 (2)
Journal of Nuclear Medicine
Vol. 63, Issue 2
February 1, 2022
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aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL Images of Veterans with Prostate Cancer
Nicholas Nickols, Aseem Anand, Kerstin Johnsson, Johan Brynolfsson, Pablo Borreli, Neil Parikh, Jesus Juarez, Lida Jafari, Mattias Eiber, Matthew Rettig
Journal of Nuclear Medicine Feb 2022, 63 (2) 233-239; DOI: 10.2967/jnumed.120.261863

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aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL Images of Veterans with Prostate Cancer
Nicholas Nickols, Aseem Anand, Kerstin Johnsson, Johan Brynolfsson, Pablo Borreli, Neil Parikh, Jesus Juarez, Lida Jafari, Mattias Eiber, Matthew Rettig
Journal of Nuclear Medicine Feb 2022, 63 (2) 233-239; DOI: 10.2967/jnumed.120.261863
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Keywords

  • 18F-DCFPyL
  • PSMA
  • aPROMISE
  • segmentation
  • quantification
  • standardization
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