Optimal [18F]FDG PET/CT Cutoff for Pathologic Complete Response in HER2-Positive Early Breast Cancer Patients Treated with Neoadjuvant Trastuzumab and Pertuzumab in the PHERGain Trial Article Swipe
Géraldine Gebhart
,
Marleen Keyaerts
,
Thomas Guiot
,
Patrick Flamen
,
Manuel Ruíz‐Borrego
,
Agostina Stradella
,
Begoña Bermejo
,
Santiago Escrivá-de-Romaní
,
Lourdes Calvo
,
Nuria Ribelles
,
María Fernandez-Abad
,
Cinta Albacar
,
Marco Colleoni
,
Laia Garrigós
,
Manuel Atienza de Frutos
,
Florence Dalenc
,
Aleix Prat
,
Frederik Marmé
,
Peter Schmid
,
Khaldoun Kerrou
,
Sofía Braga
,
Petra Gener
,
Miguel Sampayo-Cordero
,
Javier Cortés
,
José Manuel Pérez-García
,
Antonio Llombart‐Cussac
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2967/jnumed.123.266384
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2967/jnumed.123.266384
The PHERGain trial investigated the potential of metabolic imaging to identify candidates for chemotherapy deescalation in human epidermal growth factor receptor 2 (HER2)-positive, invasive, operable breast cancer with at least 1 breast lesion evaluable by [18F]FDG PET/CT. [18F]FDG PET/CT responders were defined as patients with an SUVmax reduction (ΔSUVmax) of at least 40% in all of their target lesions after 2 cycles of trastuzumab and pertuzumab (HP) (with or without endocrine therapy). In total, 227 of 285 patients (80%) included in the HP arm showed a predefined metabolic response and received a total of 8 cycles of HP (with or without endocrine therapy). Pathologic complete response (pCR), defined as ypT0/isN0, was achieved in 37.9% of the patients. Here, we describe the secondary preplanned analysis of the best cutoff of ΔSUVmax for pCR prediction. Methods: Receiver-operating-characteristic analysis was applied to look for the most appropriate ΔSUVmax cutoff in HER2-positive early breast cancer patients treated exclusively with neoadjuvant HP (with or without endocrine therapy). Results: The ΔSUVmax capability of predicting pCR in terms of the area under the receiver-operating-characteristic curve was 72.1% (95% CI, 65.1-79.2%). The optimal ΔSUVmax cutoff was found to be 77.0%, with a 51.2% sensitivity and a 78.7% specificity. With this cutoff, 74 of 285 patients (26%) would be classified as metabolic responders, increasing the pCR rate from 37.9% (cutoff ≥ 40%) to 59.5% (44/74 patients) (P < 0.01). With this optimized cutoff, 44 of 285 patients (15.4%) would avoid chemotherapy in either the neoadjuvant or the adjuvant setting compared with 86 of 285 patients (30.2%) using the original cutoff (P < 0.001). Conclusion: In the PHERGain trial, an increased SUVmax cutoff (≥77%) after 2 cycles of exclusive HP (with or without endocrine therapy) achieves a pCR in the range of the control arm with chemotherapy plus HP (59.5% vs. 57.7%, respectively), further identifying a subgroup of patients with HER2-addicted tumors. However, the original cutoff (≥40%) maximizes the number of patients who could avoid chemotherapy.
Related Topics
Concepts
Pertuzumab
Breast cancer
Trastuzumab
Medicine
Cutoff
Receiver operating characteristic
Nuclear medicine
Neoadjuvant therapy
Cancer
Internal medicine
Oncology
Physics
Quantum mechanics
Metadata
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- Landing Page
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- https://jnm.snmjournals.org/content/jnumed/early/2024/04/04/jnumed.123.266384.full.pdf
- OA Status
- bronze
- Cited By
- 7
- References
- 22
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4393930980Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2967/jnumed.123.266384Digital Object Identifier
- Title
-
Optimal [18F]FDG PET/CT Cutoff for Pathologic Complete Response in HER2-Positive Early Breast Cancer Patients Treated with Neoadjuvant Trastuzumab and Pertuzumab in the PHERGain TrialWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-04-04Full publication date if available
- Authors
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Géraldine Gebhart, Marleen Keyaerts, Thomas Guiot, Patrick Flamen, Manuel Ruíz‐Borrego, Agostina Stradella, Begoña Bermejo, Santiago Escrivá-de-Romaní, Lourdes Calvo, Nuria Ribelles, María Fernandez-Abad, Cinta Albacar, Marco Colleoni, Laia Garrigós, Manuel Atienza de Frutos, Florence Dalenc, Aleix Prat, Frederik Marmé, Peter Schmid, Khaldoun Kerrou, Sofía Braga, Petra Gener, Miguel Sampayo-Cordero, Javier Cortés, José Manuel Pérez-García, Antonio Llombart‐CussacList of authors in order
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https://doi.org/10.2967/jnumed.123.266384Publisher landing page
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https://jnm.snmjournals.org/content/jnumed/early/2024/04/04/jnumed.123.266384.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://jnm.snmjournals.org/content/jnumed/early/2024/04/04/jnumed.123.266384.full.pdfDirect OA link when available
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Pertuzumab, Breast cancer, Trastuzumab, Medicine, Cutoff, Receiver operating characteristic, Nuclear medicine, Neoadjuvant therapy, Cancer, Internal medicine, Oncology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 3, 2024: 4Per-year citation counts (last 5 years)
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22Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.identify | 10 |
| abstract_inverted_index.included | 79 |
| abstract_inverted_index.operable | 24 |
| abstract_inverted_index.original | 259, 314 |
| abstract_inverted_index.patients | 43, 77, 151, 206, 237, 255, 308, 321 |
| abstract_inverted_index.received | 90 |
| abstract_inverted_index.receptor | 20 |
| abstract_inverted_index.response | 88, 105 |
| abstract_inverted_index.subgroup | 306 |
| abstract_inverted_index.therapy) | 284 |
| abstract_inverted_index.(<i>P</i> | 227, 261 |
| abstract_inverted_index.endocrine | 70, 101, 160, 283 |
| abstract_inverted_index.epidermal | 17 |
| abstract_inverted_index.evaluable | 33 |
| abstract_inverted_index.exclusive | 278 |
| abstract_inverted_index.increased | 270 |
| abstract_inverted_index.invasive, | 23 |
| abstract_inverted_index.maximizes | 317 |
| abstract_inverted_index.metabolic | 7, 87, 212 |
| abstract_inverted_index.optimized | 232 |
| abstract_inverted_index.patients) | 226 |
| abstract_inverted_index.patients. | 116 |
| abstract_inverted_index.potential | 5 |
| abstract_inverted_index.reduction | 47 |
| abstract_inverted_index.secondary | 121 |
| abstract_inverted_index.therapy). | 71, 102, 161 |
| abstract_inverted_index.Pathologic | 103 |
| abstract_inverted_index.candidates | 11 |
| abstract_inverted_index.capability | 165 |
| abstract_inverted_index.classified | 210 |
| abstract_inverted_index.increasing | 214 |
| abstract_inverted_index.pertuzumab | 65 |
| abstract_inverted_index.predefined | 86 |
| abstract_inverted_index.predicting | 167 |
| abstract_inverted_index.preplanned | 122 |
| abstract_inverted_index.responders | 39 |
| abstract_inverted_index.ypT0/isN0, | 109 |
| abstract_inverted_index.appropriate | 143 |
| abstract_inverted_index.exclusively | 153 |
| abstract_inverted_index.identifying | 304 |
| abstract_inverted_index.neoadjuvant | 155, 245 |
| abstract_inverted_index.prediction. | 132 |
| abstract_inverted_index.responders, | 213 |
| abstract_inverted_index.sensitivity | 195 |
| abstract_inverted_index.trastuzumab | 63 |
| abstract_inverted_index.65.1-79.2%). | 182 |
| abstract_inverted_index.chemotherapy | 13, 241, 296 |
| abstract_inverted_index.deescalation | 14 |
| abstract_inverted_index.investigated | 3 |
| abstract_inverted_index.specificity. | 199 |
| abstract_inverted_index.HER2-addicted | 310 |
| abstract_inverted_index.HER2-positive | 147 |
| abstract_inverted_index.chemotherapy. | 325 |
| abstract_inverted_index.respectively), | 302 |
| abstract_inverted_index.<b>Methods:</b> | 133 |
| abstract_inverted_index.<b>Results:</b> | 162 |
| abstract_inverted_index.(HER2)-positive, | 22 |
| abstract_inverted_index.SUV<sub>max</sub> | 46, 271 |
| abstract_inverted_index.<b>Conclusion:</b> | 264 |
| abstract_inverted_index.[<sup>18</sup>F]FDG | 35, 37 |
| abstract_inverted_index.ΔSUV<sub>max</sub> | 129, 144, 164, 185 |
| abstract_inverted_index.(ΔSUV<sub>max</sub>) | 48 |
| abstract_inverted_index.Receiver-operating-characteristic | 134 |
| abstract_inverted_index.receiver-operating-characteristic | 176 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 7 |
| institutions_distinct_count | 26 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.93506035 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |