[68Ga]Ga-HER2 Affibody PET/CT for early prediction of neoadjuvant therapy outcome in HER2-positive breast cancer

[68Ga]Ga-HER2 Affibody PET/CT for early prediction of neoadjuvant therapy outcome in HER2-positive breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article [ 68 Ga]Ga-HER2 Affibody PET/CT for early prediction of neoadjuvant therapy outcome in HER2-positive breast cancer Yuhan Sun, Ran An, Ruoxi Yang, Xiao Pang, Xiaoshan Chen, Mengjiao Wang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8736870/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in EJNMMI Research → Version 1 posted 5 You are reading this latest preprint version Abstract Background Neoadjuvant therapy (NAT) has become a standard treatment for HER2-positive breast cancer. However, patient responses vary substantially, and reliable methods for early response assessment are still lacking. This study aimed to evaluate the value of [ 68 Ga]Ga-HER2 Affibody PET/CT for the early assessment of treatment outcome by predicting pathological complete response (pCR) in HER2-positive breast cancer. Results 32 of the 54 enrolled patients achieved pCR (59.3%). Following two NAT cycles, [ 68 Ga]Ga-HER2 Affibody PET/CT parameters decreased from baseline in all patients ( P < 0.001). Early percentage changes in PET parameters (ΔSUV%, ΔSUL%, ΔTLA%, ΔTBR%) and their absolute values after the second NAT cycle were associated with pCR (r range: -0.658 to -0.273; P < 0.05). ΔTBR% demonstrated the best predictive value for pCR (AUC = 0.918), with 93.8% sensitivity and 86.4% specificity at a cutoff of -70.5%. In contrast, tumor size assessment based on RECIST 1.1 showed lower predictive performance, with a sensitivity of 56.3% (18/32) and a specificity of 45.5% (10/22). Conclusion This study demonstrates the potential of [ 68 Ga]Ga-HER2 Affibody PET/CT to predict NAT outcome early in HER2-positive breast cancer, which could facilitate subsequent treatment optimization. Breast cancer HER2 PET/CT Pathological complete response Neoadjuvant therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction According to the latest (2022) global cancer figures from GLOBOCAN, breast cancer is the second most commonly diagnosed cancer worldwide and the leading cause of cancer-related death in women [1]. Human epidermal growth factor receptor 2 (HER2) is overexpressed in approximately 20–25% of newly diagnosed breast cancer cases, which correlates with a poor prognosis and reduced overall survival [2]. For these patients, neoadjuvant therapy (NAT) with dual anti-HER2 agents and chemotherapy constitutes the standard of care, reducing the surgical extent while improving survival and quality of life [3]. Given the high cost and significant variation in the individual efficacy of NAT, the early identification of patients likely to benefit from it is critical [4]. Pathological complete response (pCR) is the most critical and objective endpoint for assessing NAT efficacy and is recognized as a strong prognostic factor for predicting both disease-free and overall survival in patients with breast cancer [5]. Therefore, early and accurate prediction of pCR is essential for formulating subsequent treatment strategies [6]. Currently, however, pCR confirmation relies entirely on histopathological assessment of surgical specimens obtained after treatment completion, with no recognized evaluation method available for accurate prediction in the early treatment course (after two or four courses of treatment) [7]. Although advanced imaging techniques, such as MRI, are valuable for assessing pathological response after NAT for breast cancer, their predictive accuracy varies across studies [8-9]. Therefore, developing methods that can reliably predict pCR early in the treatment process is of great clinical importance. PET/CT quantifies biochemical processes using radiolabeled targeted probes, enabling dynamic and noninvasive monitoring of tumor morphology and function [10]. Building on this capability, HER2-targeted molecular imaging allows for precise and systemic assessment of HER2 expression through specific receptor binding. Among available targeting strategies, the Affibody molecular scaffold offers distinct advantages, including high binding affinity and low molecular weight (approximately 6.5 kDa), which facilitate rapid plasma clearance [11]. This favorable pharmacokinetic profile effectively circumvents the slow clearance and high radiation exposure typically associated with antibody-based tracers such as trastuzumab [12]. Preclinical data further support the favorable tolerability and safety profile, which underscores its translational potential [13]. However, the clinical utility of [ 68 Ga]Ga-HER2 Affibody PET/CT in predicting the efficacy of NAT remains remains to be elucidated. This study aimed to evaluate the ability of [ 68 Ga]Ga-HER2 Affibody PET/CT to predict early the pathological response to NAT and assess treatment outcome in patients with HER2-positive breast cancer, particularly after two cycles. These findings are expected to provide a foundation for the optimization of therapeutic strategies. Materials and methods Patients and study design This single-center study was approved by the institutional ethics committee (Approval No. 2022054). Consecutively enrolled patients provided written informed consent between June 2023 and August 2025. The inclusion criteria were as follows: (1) women aged 18–75 years; (2) biopsy-confirmed HER2-positive breast cancer, defined as an immunohistochemistry (IHC) score of 3+ or 2+ with a positive fluorescence in situ hybridization (FISH) result, in accordance with the American Society of Clinical Oncology (ASCO) guidelines [14]; (3) no prior treatment before baseline [ 68 Ga]Ga-HER2 Affibody PET/CT; (4) all patients underwent at least two protocol-defined PET/CT scans: PET 1 (baseline) and PET 2 (after completing two cycles of NAT); and (5) completion of the full NAT course followed by surgical resection. The exclusion criteria included the following: (1) HER2-low-expressing or HER2-negative breast cancer; (2) concurrent other malignancies; (3) unwillingness to undergo the protocol-specified PET/CT imaging; or (4) PET/CT performed outside the predefined study time windows. Treatment schemes NAT was administered according to established guidelines [15]. All enrolled patients with HER2-positive breast cancer completed the full NAT course. The treatment backbone consistently consisted of dual HER2 blockade combined with chemotherapy. Variations in the specific regimens arose from individualized adaptations of the chemotherapy components and other targeted agents, based on distinct clinical indications. The distribution of the specific neoadjuvant regimens in this cohort was as follows: TCbHP (nab-paclitaxel, carboplatin, trastuzumab, and pertuzumab) in 16 patients; AC-TH (doxorubicin, cyclophosphamide, nab-paclitaxel, trastuzumab, and pertuzumab) plus pyrotinib in 23 patients; THP (nab-paclitaxel, trastuzumab, and pertuzumab) in 6 patients; TH (nab-paclitaxel and trastuzumab) plus pyrotinib in 3 patients; and TCbH (nab-paclitaxel, carboplatin, and trastuzumab) plus pyrotinib in 6 patients. PET/CT imaging [ 68 Ga]Ga-HER2 Affibody (111-185 MBq) was intravenously administered via the antecubital vein. PET/CT imaging was initiated 50 min post-injection using a Vereos PET/CT scanner (Philips, The Netherlands). All patients were positioned supine, with their arms elevated above their heads. First, a spiral CT scan was performed from the head to the thigh base using a standard-dose protocol with the following parameters: tube voltage, 120 kV; tube current, 60 mA; pitch, 0.813; matrix, 768 × 768; slice thickness, 4 mm; and field of view (FOV) 600 mm. Immediately thereafter, PET data were acquired in three-dimensional mode at 1.5 min per bed position, with a matrix of 144 × 144, slice thickness of 3 mm, and a FOV of 576 mm. Images were reconstructed using the Ordered Subset Expectation Maximization (OSEM) algorithm with CT-based attenuation correction. All datasets (1024 × 1024 pixels) were transferred to a dedicated IntelliSpace Portal workstation for further processing and analyses. The detailed molecular structure of the radiotracer is provided in Supplementary File 1. Image analysis Serial PET/CT scans were independently evaluated by two experienced nuclear medicine physicians who were blinded to all clinical and therapeutic information. Discrepancies between the two initial readers were adjudicated by a senior nuclear medicine physician. Primary tumor or metastatic lymph node (MLN) with tracer uptake above adjacent background was considered positive. For the primary breast tumor, the maximum, mean, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak) and their lean body mass-normalized counterparts (SULmax, SULmean, and SULpeak) were first determined. The [ 68 Ga]Ga-HER2 Affibody-avid tumor volume (HTV) was subsequently delineated automatically on the PET images by applying a threshold of 40% of the SUVmax. We also derived the total lesion activity (TLA), which was calculated as the SUVmean × HTV. For lesions demonstrating low or absent radiotracer uptake, boundaries were manually defined with reference to the corresponding CT anatomy. For MLNs, when multiple involved nodes were present, the one with the highest uptake was selected, and its SUVmax was measured. To calculate the tumor-to-background ratio (TBR), we placed a 10-mm spherical ROI in the descending aorta, measured the mean standardized uptake value of the mediastinal blood pool (SUVmean-mbp), and then divided the tumor SUVmax by this value. Tumor size was assessed based on the sum of the longest diameters (SLD) of the target lesions according to RECIST 1.1 criteria [16]. Target lesions were identified on contrast-enhanced CT or MRI scans obtained from routine clinical records. For a single lesion, the long-axis diameter on the most prominent slice was measured. In multifocal disease, the SLD was calculated as the sum of the diameters of all individual foci. All parameters were measured at baseline and post-treatment, and the percentage change (Δ%) calculated as follows: [(post-value - pre-value) / pre-value] × 100%. Surgery and pathological assessment All 54 patients underwent surgical resection, which included mastectomy (n=47) and breast-conserving surgery (n=7). Axillary management consisted of sentinel lymph node biopsy (SLNB) alone (n=7), upfront axillary lymph node dissection (ALND) (n=46), or completion ALND following SLNB-confirmed metastasis (n=1). pCR was defined as the absence of invasive carcinoma in the breast and axillary lymph nodes after completion of neoadjuvant therapy, allowing for the presence of ductal carcinoma in situ (ypT0/is) [17]. Patients who did not meet this criterion were classified as non-pCR, and the extent of residual disease in these cases was further quantified using the Residual Cancer Burden (RCB) system [18]. Statistical analysis Statistical analyses were performed using IBM SPSS Statistics software (version 25.0). Continuous variables are expressed as mean ± standard deviation or median (interquartile range), based on their distribution. Categorical variables are presented as numbers (percentages). Differences in [ 68 Ga]Ga-HER2 Affibody PET/CT parameters and tumor size measurement between pCR and non-pCR groups were compared using the Mann-Whitney U test. Associations among PET/CT parameters, lesion size, and pCR status were assessed with Spearman’s correlation. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive performance of each parameter for pCR, with the optimal cutoff values determined by the Youden index. The areas under the curves (AUC) were compared using DeLong's test. All tests were two-sided, with P < 0.05 considered statistically significant. No adjustment for multiple comparisons was made because of the exploratory nature of the study design. Results Patient characteristics A total of 54 patients were included in the final analysis. The mean age of the study population was 50.96 ± 1.47 years. pCR was achieved in 32 patients (59.3%). A flowchart of the study design is presented in Fig. 1. The baseline clinical characteristics were comparable between patients who achieved pCR and those who did not (Table 1). The baseline scan (PET1) was performed 2.33 ± 1.59 days before NAT initiation, and the follow-up scan (PET2) was conducted 18.20 ± 4.49 days after two NAT cycles. Changes in [ 68 Ga]Ga-HER2 Affibody PET/CT and tumor size parameters from baseline to after two cycles of NAT The [ 68 Ga]Ga-HER2 Affibody PET/CT and tumor size parameters are summarized in Table 2 for the two time points: baseline (PET1) and after two cycles of NAT (PET2). A comparison of the median values revealed significant declines in all measured parameters (including SUVmax, SUVpeak, SUVmean, SULmax, SULpeak, SULmean, HTV, TLA, TBR, MLN SUVmax, and SLD) following treatment (all P < 0.001). Comparison of [ 68 Ga]Ga-HER2 Affibody PET/CT with tumor size parameters by NAT response Based on postoperative pathological findings, patients were stratified into pCR and non-pCR groups. The comparison of [ 68 Ga]Ga-HER2 Affibody PET/CT and tumor size parameters between the pCR and non-pCR patients is summarized in Table 3. At baseline, no significant differences in [ 68 Ga]Ga-HER2 Affibody PET/CT or tumor size parameters were detected between the groups (all P > 0.05). After two cycles of NAT, the Post-SUVmax, Post-SUVmean, Post-SUVpeak, Post-SULmax, Post-SULmean, Post-SULpeak, Post-TLA, and Post-TBR values were all lower in the pCR group than in the non-pCR group (all P < 0.05). The percentage reductions of [ 68 Ga]Ga-HER2 Affibody PET/CT parameters (including ΔSUVmax%, ΔSUVmean%, ΔSUVpeak%, ΔSULmax%, ΔSULmean%, ΔSULpeak%, ΔTLA%, and ΔTBR%) were greater in the pCR group than in the non-pCR group (all P < 0.05). The post-treatment values of HTV, MLN SUVmax, and SLD showed no significant differences between the pCR and non-pCR groups. Similarly, the percentage reductions of these parameters (ΔHTV%, ΔMLN SUVmax%, and ΔSLD%) showed no statistically significant differences between the two groups (all P > 0.05). Association of [ 68 Ga]Ga-HER2 Affibody PET/CT and tumor size parameters with pathological response Following two cycles of NAT, significant correlations were observed between pCR and [ 68 Ga]Ga-HER2 Affibody PET/CT parameters, including Post-SUVmax, Post-SUVmean, Post-SUVpeak, Post-SULmax, Post-SULmean, Post-SULpeak, Post-TLA, and Post-TBR (r = -0.437, -0.436, -0.417, -0.431, -0.443, -0.413, -0.273, -0.507; P = 0.001, P = 0.001, P = 0.002, P = 0.001, P = 0.002, P = 0.002, P = 0.046, and P < 0.001 respectively). Similarly, percentage reductions in [ 68 Ga]Ga-HER2 Affibody PET/CT parameters from baseline to post-NAT, specifically ΔSUVmax%, ΔSUVmean%, ΔSUVpeak%, ΔSULmax%, ΔSULmean%, ΔSULpeak%, ΔTLA%, and ΔTBR%, correlated with pCR (r = -0.472, -0.448, -0.484, -0.450, -0.449, -0.472, -0.318, -0.658; P < 0.001, P = 0.001, P < 0.001, P = 0.001, P = 0.001, P < 0.001, P = 0.02, and P < 0.001). The post-treatment values of HTV, MLN SUVmax, and SLD (r = -0.185, -0.193, -0.167; P = 0.180, P = 0.162, P = 0.227), as well as the percentage reductions of these parameters (ΔHTV, ΔMLN SUVmax%, and ΔSLD%) (r = -0.012, -0.208, -0.063; P = 0.879, P = 0.132, P = 0.651) showed no significant correlation with pCR (Fig. 2 and Table 3). Predictive performance of [ 68 Ga]Ga-HER2 Affibody PET/CT for pathological response Receiver operating characteristic curve analysis was performed to evaluate the accuracy of [ 68 Ga]Ga-HER2 Affibody PET/CT parameters in predicting pathological complete response and non-complete response following NAT in patients with HER2-positive breast cancer. The following parameters, measured after two cycles of NAT, were identified as significant predictors of pCR (Fig. 3): Post-treatment values: Post-SUVmax (AUC = 0.730), Post-SUVmean (AUC = 0.724), Post-SUVpeak (AUC = 0.732), Post-SULmax (AUC = 0.724), Post-SULmean (AUC = 0.733), Post-SULpeak (AUC = 0.729), Post-TLA (AUC = 0.719), Post-TBR (AUC = 0.836). Percentage changes from baseline (Δ%): ΔSUVmax% (AUC = 0.787), ΔSUVmean% (AUC = 0.770), ΔSUVpeak% (AUC = 0.793), ΔSULmax% (AUC = 0.766), ΔSULmean% (AUC = 0.777), ΔSULpeak% (AUC = 0.783), ΔTLA% (AUC = 0.762), ΔTBR% (AUC = 0.918). The predictive performance of [ 68 Ga]Ga-HER2 Affibody PET/CT for early pathological response to NAT is summarized in Table 4. ΔTBR% showed the highest accuracy for predicting pCR (AUC = 0.918, 95% CI: 0.833-1.000; P < 0.001), with 93.8% sensitivity and 86.4% specificity at the -70.5% cutoff. The ΔTBR% of [ 68 Ga]Ga-HER2 Affibody PET/CT was compared among the five NAT regimens. As shown in Fig. 4, no statistically significant differences in ΔTBR% were observed among the treatment groups. The baseline and post-treatment PET/CT images of representative pCR and non-pCR patients are shown in Figs. 5 and 6, respectively. Predictive performance of [ 68 Ga]Ga-HER2 Affibody PET/CT versus tumor size parameters in pathological response After two cycles of NAT, tumor size assessment based on RECIST 1.1 criteria demonstrated the following predictive performance for pathological response: sensitivity 56.3% (18/32), specificity 45.5% (10/22), PPV 60.0% (18/30), NPV 41.7% (10/24), and accuracy 51.9% (28/54) (Supplemental Table 1). In comparison, the optimal [ 68 Ga]Ga-HER2 Affibody PET/CT parameter, ΔTBR%, exhibited higher predictive accuracy, achieving a sensitivity of 93.8% and a specificity of 86.4% at the optimal cutoff value of -70.5%, and an AUC of 0.918, as previously reported. Discussion Neoadjuvant therapy (NAT) significantly improves the prognosis of patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer; however, interindividual variability in treatment response persists. Therefore, the early prediction of therapeutic efficacy and timely adjustment of treatment strategies are crucial for guiding clinical decision-making. This study evaluated the value of various PET-derived parameters in predicting pathological response to NAT and preliminarily demonstrated the clinical utility of [ 68 Ga]Ga-HER2 Affibody PET/CT for the early prediction of treatment efficacy in patients with HER2-positive breast cancer. Specifically, after two cycles of neoadjuvant therapy, all patients showed a significant reduction in uptake compared to baseline, consistent with the treatment efficacy criteria defined by PERCIST 1.0 [19]. Importantly, patients with lower post-treatment uptake and greater reductions had a higher probability of achieving pathological complete response (pCR) in HER2-positive breast cancer. Compared with previous HER2-targeted imaging studies using [ 64 Cu]Cu-DOTA-trastuzumab or [ 89 Zr]Zr-trastuzumab [20-21], this study not only confirms the significant association between early changes in PET/CT parameters and pCR in HER2-positive breast cancer, but also offers the key advantage of avoiding interference with ongoing anti-HER2 therapy during monitoring, since the Affibody molecule binds to a distinct HER2 epitope [22]. Building on this unique property, combined with the probe's proven safety, rapid imaging capability, and excellent tissue penetration [23], [ 68 Ga]Ga-HER2 Affibody PET/CT may enable dynamic monitoring of HER2 expression during therapy, facilitating early identification of patients with poor treatment response. The information thus obtained holds potential to inform the formulation of individualized treatment strategies. For example, if a patient shows only a small decrease in tracer uptake, treatment might be intensified or the chemotherapy regimen changed, with the goal of improving clinical outcomes and optimizing treatment plan. Published guidelines recommend [ 18 F]FDG PET/CT for staging high-risk breast cancer and monitoring HER2-targeted therapy response [24]. However, a prior meta-analysis showed that [ 18 F]FDG PET/CT has high sensitivity but limited specificity for predicting pCR in patients with HER2-positive breast cancer [25]. Guo et al. demonstrated that for HER2-overexpressing tumors, HER2-targeted PET/CT probes exhibit significantly superior diagnostic performance compared to [ 18 F]FDG, particularly in detecting primary tumors and metastatic lesions in lymph nodes, bone, and liver [26]. This study further established that the post-treatment quantitative parameters obtained after two cycles of NAT on [ 68 Ga]Ga-HER2 Affibody PET/CT, along with their relative changes from baseline, served as significant predictors of pCR in patients with HER2-positive breast cancer. Among these parameters, ΔTBR% demonstrated the highest diagnostic accuracy. A ΔTBR% reduction beyond the –70.5% threshold indicated effective NAT and increased pCR probability. Furthermore, in semi-quantitative assessments, TBR exhibited superior robustness against interference compared with SUVmax and SUVmean, more accurately reflecting the true biological distribution, thereby enhancing the precision of treatment response monitoring [27]. Moreover, a comparative analysis of ΔTBR% across the five treatment regimens revealed no significant intergroup differences, indicating that the early treatment response assessed using HER2-targeted PET is independent of the treatment regimen. Previous studies have indicated that RECIST 1.1, while serving as a widely used imaging standard for evaluating treatment response in solid tumors, presents certain limitations when applied to breast cancer therapy [28]. Our findings demonstrate that although tumor size generally decreased after two cycles of NAT, imaging evaluation based on RECIST 1.1 criteria exhibited limited sensitivity and specificity for the early prediction of pCR. This observed constraint may be explained by several factors. Pathological alterations induced by chemotherapy, including reduced cellularity, necrosis, and fibrosis, do not consistently translate to measurable changes in overall tumor size [19]. Moreover, conventional unidimensional measurements fail to adequately characterize non-concentric regression patterns, such as the scattered and heterogeneous shrinkage seen in nest-like or dendritic morphological changes [29]. As a result, strict adherence to the RECIST criteria in this setting could lead to underestimation of the true treatment effect. Therefore, it is necessary to incorporate functional molecular information into the precision evaluation system to enhance the early identification of patients responsive to treatment, thereby providing a more reliable basis for guiding subsequent clinical decisions. This study has several limitations. Its single-center design necessitates validation in multicenter cohorts. Furthermore, the limited cohort size and the histological imbalance within the invasive carcinoma group, which was predominantly composed of invasive ductal carcinoma, precluded meaningful stratified analyses according to histological subtype. Therefore, future studies should involve larger patient cohorts with greater clinicopathological diversity. Conclusion This preliminary study indicates that [ 68 Ga]Ga-HER2 Affibody PET/CT imaging can rapidly detect tumor changes after two cycles of neoadjuvant therapy, demonstrating its potential for the early prediction of treatment outcome in HER2‑positive breast cancer. Future large-scale prospective studies are necessary to further validate the value of [ 68 Ga]Ga-HER2 Affibody PET/CT in evaluating the efficacy of neoadjuvant therapy in HER2-positive breast cancer. Abbreviations HER2 Human epidermal growth factor receptor 2 NAT Neoadjuvant therapy pCR Pathological complete response SLD Sum of the longest diameters SUVmax Maximum standardized uptake value SUVmean Mean standardized uptake value SUVpeak Peak standardized uptake value SULmax Lean body mass–normalized maximum standardized uptake value SULmean Lean body mass–normalized mean standardized uptake value SULpeak Lean body mass–normalized peak standardized uptake value HTV [ 68 Ga]Ga-HER2 affibody-avid tumour volume TLA Total lesion activity TBR Tumor-to-background ratio MLN Metastatic lymph node AUC Area under the curve SLNB Sentinel lymph node biopsy ALND Axillary lymph node dissection H&E Hematoxylin and eosin Declarations Acknowledgements We gratefully acknowledge all study participants. Author contributions All authors have read and approved the manuscript. Y.S. and R.A. contributed equally as co-first authors. The study concept was proposed by X.Z. Database search was performed by Y.S., X.C., M.W. and J.Z. Analysis and interpretation of data were conducted by R.A., R.Y., X.P., Q.F., and X.C. The manuscript was drafted by X.Z., Y.S., and R.A. Revision of the manuscript was performed by X.Z., Y.Z., Y.S., Y.L., J.H., and N.W. Funding This work was supported by the Hebei Provincial Natural Science Foundation, the Jing-Jin-Ji Special Projects for Basic Research Cooperation (Grant No. H2018206600), and the Hengrui Hebei Collaborative Innovation Program in Medical Science (Grant No. HR202502074). Data availability All data and materials are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate This study was approved by the institutional ethics committee (Approval No. 2022054). Informed Consent was obtained from all patients participating in the study. Consent for publication The participants in the study were informed and consented to the possibility of research publication. 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From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50 Suppl 1(Suppl 1):122S-50S. https://doi.org/10.2967/jnumed.108.057307. Mortimer JE, Bading JR, Frankel PH, Carroll MI, Yuan Y, Park JM, et al. Use of 64Cu-DOTA-Trastuzumab PET to Predict Response and Outcome of Patients Receiving Trastuzumab Emtansine for Metastatic Breast Cancer: A Pilot Study. J Nucl Med. 2022;63(8):1145–8. https://doi.org/10.2967/jnumed.121.262940. Yeh R, O'Donoghue JA, Jayaprakasam VS, Mauguen A, Min R, Park S, et al. First-in-Human Evaluation of Site-Specifically Labeled 89Zr-Pertuzumab in Patients with HER2-Positive Breast Cancer. J Nucl Med. 2024;65(3):386-393. https://doi.org/10.2967/jnumed.123.266392. Orlova A, Wållberg H, Stone-Elander S, Tolmachev V. On the selection of a tracer for PET imaging of HER2-expressing tumors: direct comparison of a 124I-labeled affibody molecule and trastuzumab in a murine xenograft model. J Nucl Med. 2009;50(3):417-425. https://doi.org/10.2967/jnumed.108.057919. Wållberg H, Grafström J, Cheng Q, Lu L, Martinsson Ahlzén HS, Samén E, et al. HER2-positive tumors imaged within 1 hour using a site-specifically 11C-labeled Sel-tagged affibody molecule. J Nucl Med. 2012;53 (9):1446-1453. https://doi.org/ 10.2967/jnumed.111.102194. Diwanji D, Ray K, Hylton N. 18F-FDG PET/CT Predicts Response to HER2-directed Neoadjuvant Therapy. Radiol Imaging Cancer. 2021;3(5):e219021. https://doi.org/10.1148/rycan.2021219021. Tian F, Shen G, Deng Y, Diao W, Jia Z. The accuracy of 18F-FDG PET/CT in predicting the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis and systematic review. Eur Radiol. 2017;27(11):4786–96. https://doi.org/10.1007/s00330-017-4831-y. Guo X, Zhou N, Liu J, Ding J, Liu T, Song G, et al. Comparison of an Affibody-based Molecular Probe and 18F-FDG for Detecting HER2-Positive Breast Cancer at PET/CT. Radiology. 2024;311(3):e232209. https://doi.org/10.1148/radiol.232209. Chen R, Yang X, Yu X, Zhou X, Ng YL, Zhao H, et al. Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [ 68 Ga]Ga-FAPI-04. Eur J Nucl Med Mol Imaging. 2023;50(3):929–36. https://doi.org/10.1007/s00259-022-06010-5. Tateishi U, Miyake M, Nagaoka T, Terauchi T, Kubota K, Kinoshita T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging--prospective assessment. Radiology. 2012;263(1):53-63. https://doi.org/10.1148/radiol.12111177. Fukada I, Araki K, Kobayashi K, Shibayama T, Takahashi S, Gomi N, et al. Pattern of Tumor Shrinkage during Neoadjuvant Chemotherapy Is Associated with Prognosis in Low-Grade Luminal Early Breast Cancer. Radiology. 2018;286(1):49-57. https://doi.org/10.1148/radiol.2017161548. Tables Tables 1 to 4 are available in the Supplementary Files section. Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in EJNMMI Research → Version 1 posted Editorial decision: Major Revision 20 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 30 Jan, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8736870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582837574,"identity":"df72f7eb-7797-4473-8c56-37931328b4c6","order_by":0,"name":"Yuhan Sun","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Sun","suffix":""},{"id":582837575,"identity":"17c09cdf-8716-4657-9d63-b3719f4202d9","order_by":1,"name":"Ran An","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical 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04:15:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8736870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8736870/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13550-026-01424-w","type":"published","date":"2026-04-02T15:58:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101789090,"identity":"b8ea011b-5196-4224-aa0b-1b0f030da5f4","added_by":"auto","created_at":"2026-02-03 15:56:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":865328,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/a116d718d7d4eb9ab6a344a2.png"},{"id":101789136,"identity":"9fe26cde-d590-4485-98cb-d2a63b2a5e7d","added_by":"auto","created_at":"2026-02-03 15:56:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9419251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAssociation of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT and tumor size parameters with pathological response. Red circles indicate positive correlations, while blue circles represent negative correlations. Darker shades denote stronger correlation magnitudes. \u003cem\u003eP\u003c/em\u003e values reflect the statistical significance of the correlations. *\u003cem\u003e P \u003c/em\u003e\u0026lt; 0.05. **\u003cem\u003e P \u003c/em\u003e\u0026lt; 0.01. ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/47dbf65e054945dceea46ac7.png"},{"id":101880940,"identity":"9407bce2-ca4e-498d-a429-5ce5a9cdb8f2","added_by":"auto","created_at":"2026-02-04 15:08:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4161698,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves evaluating early predictive accuracy of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters for pathological response to neoadjuvant therapy.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/89fab21f59c8b60fa258ffc0.png"},{"id":101789093,"identity":"59d26594-6cae-4c49-b504-317b12ebbdd7","added_by":"auto","created_at":"2026-02-03 15:56:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118605,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ΔTBR% among neoadjuvant therapy regimens.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/aeab0601f28d0270d88ef61d.png"},{"id":101789037,"identity":"238a931a-3219-4bb8-9fb2-69a6ed5666f9","added_by":"auto","created_at":"2026-02-03 15:55:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11988425,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT of a 64-year-old woman with left-sided invasive ductal carcinoma and who achieved pCR. (\u003cstrong\u003eA) \u003c/strong\u003eThe baseline [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT scan shows a tumor lesion (red arrow; SUVmax, 3.22; TBR, 1.69).\u003cstrong\u003e (B) \u003c/strong\u003eAfter 2 NAT cycles, the [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT image demonstrates a marked decrease in tumor lesion radioactivity uptake (SUVmax, 1.05; TBR, 0.42)\u003cstrong\u003e. (C) \u003c/strong\u003eDiagnostic biopsy of invasive ductal carcinoma shows tumor nests with nuclear atypia and cytoplasmic vacuolization. HER2 immunohistochemistry shows strong, complete membranous staining (IHC 3+), establishing the HER2-positive diagnosis. \u003cstrong\u003e(D)\u003c/strong\u003e The post-therapy resection specimen reveals only fibrotic stroma with no residual carcinoma, confirming a pathological complete response (pCR) to neoadjuvant chemotherapy.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/61cb937f9cce15eb77df0aa2.png"},{"id":101789049,"identity":"702fcc03-da08-49f0-b8cc-6cf3e520fdc8","added_by":"auto","created_at":"2026-02-03 15:55:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17636143,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT of a 58-year-old woman with right-sided invasive ductal carcinoma and who did not achieve pCR. (\u003cstrong\u003eA)\u003c/strong\u003e The baseline [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT scan shows a tumor lesion (red arrow; SUVmax, 4.26; TBR, 0.91). (\u003cstrong\u003eB)\u003c/strong\u003e After 2 NAT cycles, the [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 PET/CT image demonstrates the tumor lesion with a minimal change in uptake (red arrow; SUVmax, 2.37; TBR, 0.37).\u003cstrong\u003e (C) \u003c/strong\u003eDiagnostic biopsy of invasive ductal carcinoma shows tumor nests with nuclear atypia and cytoplasmic vacuolization. HER2 immunohistochemistry shows strong, complete membranous staining (IHC 3+), confirming HER2 positivity.\u003cstrong\u003e (D) \u003c/strong\u003eThe post-neoadjuvant therapy resection specimen shows a partial treatment response, featuring residual tumor nests amidst stromal fibrosis and lymphocytic infiltration. HER2 IHC confirms retained strong (3+) membranous expression.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/8af2b3412ac929e65ec9b627.png"},{"id":106344802,"identity":"37960c11-18bb-4e1f-985b-e3edd0575bc4","added_by":"auto","created_at":"2026-04-07 16:16:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":41042871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/808b9f0d-e25b-4a7c-9168-e836ccb072d8.pdf"},{"id":101789126,"identity":"4303f113-15d0-4b59-855b-38ab7221e9e9","added_by":"auto","created_at":"2026-02-03 15:56:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20846,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/2c6b2b01b0b87ffe5e5c61b8.docx"},{"id":101789121,"identity":"78b2ae55-ea1e-42f0-9d50-33c8d7aa76a6","added_by":"auto","created_at":"2026-02-03 15:56:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18612,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/f65d6e295c964e2a25607fa0.docx"},{"id":101789070,"identity":"e90d5bae-1566-48d4-bfea-b5b23309b702","added_by":"auto","created_at":"2026-02-03 15:56:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22102,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/2d4d51c748dfb97b95b02e38.docx"},{"id":101789138,"identity":"1596b0b7-7659-4c19-b322-ab845024015d","added_by":"auto","created_at":"2026-02-03 15:56:20","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19597,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/c306b22c842886b247b400cd.docx"},{"id":101789072,"identity":"6d0b6d58-af51-4bda-905a-39852f28fa3b","added_by":"auto","created_at":"2026-02-03 15:56:04","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18168,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8736870/v1/1409671649f75f2075ecacab.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa]Ga-HER2 Affibody PET/CT for early prediction of neoadjuvant therapy outcome in HER2-positive breast cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the latest (2022) global cancer figures from GLOBOCAN, breast cancer is the second most commonly diagnosed cancer worldwide and the leading cause of cancer-related death in women\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e[1]. Human epidermal growth factor receptor 2 (HER2) is overexpressed in approximately 20\u0026ndash;25% of newly diagnosed breast cancer cases, which correlates with a poor prognosis and reduced overall survival [2]. For these patients, neoadjuvant therapy (NAT) with dual anti-HER2 agents and chemotherapy constitutes the standard of care, reducing the surgical extent while improving survival and quality of life [3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the high cost and significant variation in the individual efficacy of NAT, the early identification of patients likely to benefit from it is critical\u0026nbsp;[4]. Pathological complete response (pCR) is the most critical and objective endpoint for assessing NAT efficacy and is recognized as a strong prognostic factor for predicting both disease-free and overall survival in patients with breast cancer [5]. Therefore, early and accurate prediction of pCR is essential for formulating subsequent treatment strategies [6]. Currently, however, pCR confirmation relies entirely on histopathological assessment of surgical specimens obtained after treatment completion, with no recognized evaluation method available for accurate prediction in the early treatment course (after two or four courses of treatment) [7]. Although advanced imaging techniques, such as MRI, are valuable for assessing pathological response after NAT for breast cancer, their predictive accuracy varies across studies [8-9].\u0026nbsp;Therefore, developing methods that can reliably predict pCR early in the treatment process is of great clinical importance.\u003c/p\u003e\n\u003cp\u003ePET/CT quantifies biochemical processes using radiolabeled targeted probes, enabling dynamic and noninvasive monitoring of tumor morphology and function [10]. Building on this capability, HER2-targeted molecular imaging allows for precise and systemic assessment of HER2 expression through specific receptor binding. Among available targeting strategies, the Affibody molecular scaffold offers distinct advantages, including high binding affinity and low molecular weight (approximately 6.5 kDa), which facilitate rapid plasma clearance [11]. This favorable pharmacokinetic profile effectively circumvents the slow clearance and high radiation exposure typically associated with antibody-based tracers such as trastuzumab [12]. Preclinical data further support the favorable tolerability and safety profile, which underscores its translational potential [13]. However, the clinical utility of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT in predicting the efficacy of NAT remains remains to be elucidated.\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the ability of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT to predict early the pathological response to NAT and assess treatment outcome in patients with HER2-positive breast cancer, particularly after two cycles. These findings are expected to provide a foundation for the optimization of therapeutic strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center study was approved by the institutional ethics committee (Approval No. 2022054). Consecutively enrolled patients provided written informed consent between June 2023 and August 2025.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (1) women aged 18\u0026ndash;75 years; (2) biopsy-confirmed HER2-positive breast cancer, defined as an immunohistochemistry (IHC) score of 3+ or 2+ with a positive fluorescence in situ hybridization (FISH) result, in accordance with the American Society of Clinical Oncology (ASCO) guidelines [14]; (3) no prior treatment before baseline [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT; (4) all patients underwent at least two protocol-defined PET/CT scans: PET 1 (baseline) and PET 2 (after completing two cycles of NAT); and (5) completion of the full NAT course followed by surgical resection. The exclusion criteria included the following: (1) HER2-low-expressing or HER2-negative breast cancer; (2) concurrent other malignancies; (3) unwillingness to undergo the protocol-specified PET/CT imaging; or (4) PET/CT performed outside the predefined study time windows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment schemes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNAT was administered according to established guidelines [15]. All enrolled patients with HER2-positive breast cancer completed the full NAT course. The treatment backbone consistently consisted of dual HER2 blockade combined with chemotherapy. Variations in the specific regimens arose from individualized adaptations of the chemotherapy components and other targeted agents, based on distinct clinical indications. The distribution of the specific neoadjuvant regimens in this cohort was as follows: TCbHP (nab-paclitaxel, carboplatin, trastuzumab, and pertuzumab) in 16 patients; AC-TH (doxorubicin, cyclophosphamide, nab-paclitaxel, trastuzumab, and pertuzumab) plus pyrotinib in 23 patients; THP (nab-paclitaxel, trastuzumab, and pertuzumab) in 6 patients; TH (nab-paclitaxel and trastuzumab) plus pyrotinib in 3 patients; and TCbH (nab-paclitaxel, carboplatin, and trastuzumab) plus pyrotinib in 6 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePET/CT imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody (111-185 MBq) was intravenously administered via the antecubital vein. PET/CT imaging was initiated 50 min post-injection using a Vereos PET/CT scanner (Philips, The Netherlands). All patients were positioned supine, with their arms elevated above their heads. First, a spiral CT scan was performed from the head to the thigh base using a standard-dose protocol with the following parameters: tube voltage, 120 kV; tube current, 60 mA; pitch, 0.813; matrix, 768\u0026nbsp;\u0026times;\u0026nbsp;768; slice thickness, 4 mm; and field of view (FOV) 600 mm. Immediately thereafter, PET data were acquired in three-dimensional mode at 1.5 min per bed position, with a matrix of 144\u0026nbsp;\u0026times;\u0026nbsp;144, slice thickness of 3 mm, and a FOV of 576 mm. Images were reconstructed using the Ordered Subset Expectation Maximization (OSEM) algorithm with CT-based attenuation correction. All datasets (1024\u0026nbsp;\u0026times;\u0026nbsp;1024 pixels) were transferred to a dedicated IntelliSpace Portal workstation for further processing and analyses. The detailed molecular structure of the radiotracer is provided in Supplementary File 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerial PET/CT scans were independently evaluated by two experienced nuclear medicine physicians who were blinded to all clinical and therapeutic information. Discrepancies between the two initial readers were adjudicated by a senior nuclear medicine physician. Primary tumor or metastatic lymph node (MLN) with tracer uptake above adjacent background was considered positive. For the primary breast tumor, the maximum, mean, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak) and their lean body mass-normalized counterparts (SULmax, SULmean, and SULpeak) were first determined. The [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody-avid tumor volume (HTV) was subsequently delineated automatically on the PET images by applying a threshold of 40% of the SUVmax. We also derived the total lesion activity (TLA), which was calculated as the SUVmean \u0026times; HTV. For lesions demonstrating low or absent radiotracer uptake, boundaries were manually defined with reference to the corresponding CT anatomy. For MLNs, when multiple involved nodes were present, the one with the highest uptake was selected, and its SUVmax was measured. To calculate the tumor-to-background ratio (TBR), we placed a 10-mm spherical ROI in the descending aorta, measured the mean standardized uptake value of the mediastinal blood pool (SUVmean-mbp), and then divided the tumor SUVmax by this value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTumor size was assessed based on the sum of the longest diameters (SLD) of the target lesions according to RECIST 1.1 criteria [16]. Target lesions were identified on contrast-enhanced CT or MRI scans obtained from routine clinical records. For a single lesion, the long-axis diameter on the most prominent slice was measured. In multifocal disease, the SLD was calculated as the sum of the diameters of all individual foci.\u003c/p\u003e\n\u003cp\u003eAll parameters were measured at baseline and post-treatment, and the percentage change (\u0026Delta;%) calculated as follows: [(post-value - pre-value) / pre-value] \u0026times; 100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery and pathological assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 54 patients underwent surgical resection, which included mastectomy (n=47) and breast-conserving surgery (n=7). Axillary management consisted of sentinel lymph node biopsy (SLNB) alone (n=7), upfront axillary lymph node dissection (ALND) (n=46), or completion ALND following SLNB-confirmed metastasis (n=1).\u003c/p\u003e\n\u003cp\u003epCR was defined as the absence of invasive carcinoma in the breast and axillary lymph nodes after completion of neoadjuvant therapy, allowing for the presence of ductal carcinoma in situ (ypT0/is) [17]. Patients who did not meet this criterion were classified as non-pCR, and the extent of residual disease in these cases was further quantified using the Residual Cancer Burden (RCB) system\u0026nbsp;[18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics software (version 25.0). Continuous variables are expressed as mean \u0026plusmn; standard deviation or median (interquartile range), based on their distribution. Categorical variables are presented as numbers (percentages).\u0026nbsp;Differences in [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters and tumor size measurement between pCR and non-pCR groups were compared using the Mann-Whitney U test. Associations among PET/CT parameters, lesion size, and pCR status were assessed with Spearman\u0026rsquo;s correlation. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive performance of each parameter for pCR, with the optimal cutoff values determined by the Youden index. The areas under the curves (AUC) were compared using DeLong\u0026apos;s test. All tests were two-sided, with \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 considered statistically significant. No adjustment for multiple comparisons was made because of the exploratory nature of the study design.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 54 patients were included in the final analysis. The mean age of the study population was 50.96 \u0026plusmn; 1.47 years. pCR was achieved in 32 patients (59.3%). A flowchart of the study design is presented in Fig. 1. The baseline clinical characteristics were comparable between patients who achieved pCR and those who did not (Table 1). The baseline scan (PET1) was performed 2.33 \u0026plusmn; 1.59 days before NAT initiation, and the follow-up scan (PET2) was conducted 18.20 \u0026plusmn; 4.49 days after two NAT cycles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT and tumor size parameters from baseline to after two cycles of NAT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT and tumor size parameters are summarized in Table 2 for the two time points: baseline (PET1) and after two cycles of NAT (PET2). A comparison of the median values revealed significant declines in all measured parameters (including SUVmax, SUVpeak, SUVmean, SULmax, SULpeak, SULmean, HTV, TLA, TBR,\u0026nbsp;MLN SUVmax, and SLD) following treatment (all \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT with tumor size parameters by NAT response\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on postoperative pathological findings, patients were stratified into pCR and non-pCR groups. The comparison of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT and tumor size parameters between the pCR and non-pCR patients is summarized in Table 3. At baseline,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eno significant differences in [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT or tumor size parameters were detected between the groups (all\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05). After two cycles of NAT, the Post-SUVmax, Post-SUVmean, Post-SUVpeak, Post-SULmax, Post-SULmean, Post-SULpeak, Post-TLA, and Post-TBR values were all lower in the pCR group than in the non-pCR group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The percentage reductions of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters (including \u0026Delta;SUVmax%, \u0026Delta;SUVmean%, \u0026Delta;SUVpeak%, \u0026Delta;SULmax%, \u0026Delta;SULmean%, \u0026Delta;SULpeak%, \u0026Delta;TLA%, and \u0026Delta;TBR%) were greater in the pCR group than in the non-pCR group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe post-treatment values of HTV, MLN SUVmax, and SLD showed no significant differences between the pCR and non-pCR groups. Similarly, the percentage reductions of these parameters (\u0026Delta;HTV%, \u0026Delta;MLN SUVmax%, and \u0026Delta;SLD%) showed no statistically significant differences between the two groups (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT and tumor size parameters with pathological response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing two cycles of NAT, significant correlations were observed between pCR and [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters, including Post-SUVmax, Post-SUVmean, Post-SUVpeak, Post-SULmax, Post-SULmean, Post-SULpeak, Post-TLA, and Post-TBR (r = -0.437, -0.436, -0.417, -0.431, -0.443, -0.413, -0.273, -0.507; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.001, \u003cem\u003eP\u003c/em\u003e = 0.001,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.002, \u003cem\u003eP\u003c/em\u003e = 0.001, \u003cem\u003eP\u003c/em\u003e = 0.002, \u003cem\u003eP\u003c/em\u003e = 0.002, \u003cem\u003eP\u003c/em\u003e = 0.046, and\u003cem\u003e\u0026nbsp;P \u0026lt;\u003c/em\u003e 0.001 respectively). Similarly, percentage reductions in [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters from baseline to post-NAT, specifically \u0026Delta;SUVmax%, \u0026Delta;SUVmean%, \u0026Delta;SUVpeak%, \u0026Delta;SULmax%, \u0026Delta;SULmean%, \u0026Delta;SULpeak%, \u0026Delta;TLA%, and \u0026Delta;TBR%, correlated with pCR (r = -0.472, -0.448, -0.484, -0.450, -0.449, -0.472, -0.318, -0.658; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.001, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eP\u003c/em\u003e = 0.001, \u003cem\u003eP\u003c/em\u003e = 0.001, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.02, and \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe post-treatment values of HTV, MLN SUVmax, and SLD (r = -0.185, -0.193, -0.167; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.180, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.162, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.227), as well as the percentage reductions of these parameters (\u0026Delta;HTV, \u0026Delta;MLN SUVmax%, and \u0026Delta;SLD%) (r = -0.012, -0.208, -0.063; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.879, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.132, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.651) showed no significant correlation with pCR (Fig. 2 and Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performance\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT for pathological response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic curve analysis was performed to evaluate the accuracy of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters in predicting pathological complete response and non-complete response following NAT in patients with HER2-positive breast cancer. The following parameters, measured after two cycles of NAT, were identified as significant predictors of pCR (Fig. 3): Post-treatment values: Post-SUVmax (AUC = 0.730), Post-SUVmean (AUC = 0.724), Post-SUVpeak (AUC = 0.732), Post-SULmax (AUC = 0.724), Post-SULmean (AUC = 0.733), Post-SULpeak (AUC = 0.729), Post-TLA (AUC = 0.719), Post-TBR (AUC = 0.836). Percentage changes from baseline (\u0026Delta;%): \u0026Delta;SUVmax% (AUC = 0.787), \u0026Delta;SUVmean% (AUC = 0.770), \u0026Delta;SUVpeak% (AUC = 0.793), \u0026Delta;SULmax% (AUC = 0.766), \u0026Delta;SULmean% (AUC = 0.777), \u0026Delta;SULpeak% (AUC = 0.783), \u0026Delta;TLA% (AUC = 0.762), \u0026Delta;TBR% (AUC = 0.918).\u003c/p\u003e\n\u003cp\u003eThe predictive performance of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT for early pathological response to NAT is summarized in Table 4. \u0026Delta;TBR% showed the highest accuracy for predicting pCR (AUC = 0.918, 95% CI: 0.833-1.000; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e 0.001), with 93.8% sensitivity and 86.4% specificity at the -70.5% cutoff.\u003c/p\u003e\n\u003cp\u003eThe \u0026Delta;TBR% of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT was compared among the five NAT regimens. As shown in Fig. 4, no statistically significant differences in \u0026Delta;TBR% were observed among the treatment groups.\u003c/p\u003e\n\u003cp\u003eThe baseline and post-treatment PET/CT images of representative pCR and non-pCR patients are shown in Figs. 5 and 6, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performance of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT versus tumor size parameters in pathological response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter two cycles of NAT, tumor size assessment based on RECIST 1.1 criteria demonstrated the following predictive performance for pathological response: sensitivity 56.3% (18/32), specificity 45.5% (10/22), PPV 60.0% (18/30), NPV 41.7% (10/24), and accuracy 51.9% (28/54) (Supplemental Table 1). In comparison, the optimal [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameter, \u0026Delta;TBR%, exhibited higher predictive accuracy, achieving a sensitivity of 93.8% and a specificity of 86.4% at the optimal cutoff value of -70.5%, and an AUC of 0.918, as previously reported.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNeoadjuvant therapy (NAT) significantly improves the prognosis of patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer; however, interindividual variability in treatment response persists. Therefore, the early prediction of therapeutic efficacy and timely adjustment of treatment strategies are crucial for guiding clinical decision-making. This study evaluated the value of various PET-derived parameters in predicting pathological response to NAT and preliminarily demonstrated the clinical utility of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT for the early prediction of treatment efficacy in patients with HER2-positive breast cancer. Specifically, after two cycles of neoadjuvant therapy, all patients showed a significant reduction in uptake compared to baseline, consistent with the treatment efficacy criteria defined by PERCIST 1.0\u0026nbsp;[19]. Importantly, patients with lower post-treatment uptake and greater reductions had a higher probability of achieving pathological complete response (pCR) in HER2-positive breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with previous HER2-targeted imaging studies using [\u003csup\u003e64\u003c/sup\u003eCu]Cu-DOTA-trastuzumab or [\u003csup\u003e89\u003c/sup\u003eZr]Zr-trastuzumab [20-21], this study not only confirms the significant association between early changes in PET/CT parameters and pCR in HER2-positive breast cancer, but also offers the key advantage of avoiding interference with ongoing anti-HER2 therapy during monitoring, since the Affibody molecule binds to a distinct HER2 epitope\u0026nbsp;[22]. Building on this unique property, combined with the probe\u0026apos;s proven safety, rapid imaging capability, and excellent tissue penetration\u0026nbsp;[23], [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT may enable dynamic monitoring of HER2 expression during therapy, facilitating early identification of patients with poor treatment response. The information thus obtained holds potential to inform the formulation of individualized treatment strategies. For example, if a patient shows only a small decrease in tracer uptake, treatment might be intensified or the chemotherapy regimen changed, with the goal of improving clinical outcomes and optimizing treatment plan.\u003c/p\u003e\n\u003cp\u003ePublished guidelines recommend [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT for staging high-risk breast cancer and monitoring HER2-targeted therapy response [24]. However, a prior meta-analysis showed that [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT has high sensitivity but limited specificity for predicting pCR in patients with HER2-positive breast cancer\u0026nbsp;[25]. Guo et al. demonstrated that for HER2-overexpressing tumors, HER2-targeted PET/CT probes exhibit significantly superior diagnostic performance compared to [\u003csup\u003e18\u003c/sup\u003eF]FDG, particularly in detecting primary tumors and metastatic lesions in lymph nodes, bone, and liver\u0026nbsp;[26]. This study further established that the post-treatment quantitative parameters obtained after two cycles of NAT on [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT, along with their relative changes from baseline, served as significant predictors of pCR in patients with HER2-positive breast cancer. Among these parameters, \u0026Delta;TBR% demonstrated the highest diagnostic accuracy. A \u0026Delta;TBR% reduction beyond the \u0026ndash;70.5% threshold indicated effective NAT and increased pCR probability. Furthermore, in semi-quantitative assessments, TBR exhibited superior robustness against interference compared with SUVmax and SUVmean, more accurately reflecting the true biological distribution, thereby enhancing the precision of treatment response monitoring\u0026nbsp;[27]. Moreover, a comparative analysis of \u0026Delta;TBR% across the five treatment regimens revealed no significant intergroup differences, indicating that the early treatment response assessed using HER2-targeted PET is independent of the treatment regimen.\u003c/p\u003e\n\u003cp\u003ePrevious studies have indicated that RECIST 1.1, while serving as a widely used imaging standard for evaluating treatment response in solid tumors, presents certain limitations when applied to breast cancer therapy [28]. Our findings demonstrate that although tumor size generally decreased after two cycles of NAT, imaging evaluation based on RECIST 1.1 criteria exhibited limited sensitivity and specificity for the early prediction of pCR. This observed constraint may be explained by several factors. Pathological alterations induced by chemotherapy, including reduced cellularity, necrosis, and fibrosis, do not consistently translate to measurable changes in overall tumor size [19]. Moreover, conventional unidimensional measurements fail to adequately characterize non-concentric regression patterns, such as the scattered and heterogeneous shrinkage seen in nest-like or dendritic morphological changes [29]. As a result, strict adherence to the RECIST criteria in this setting could lead to underestimation of the true treatment effect. Therefore, it is necessary to incorporate functional molecular information into the precision evaluation system to enhance the early identification of patients responsive to treatment, thereby providing a more reliable basis for guiding subsequent clinical decisions.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Its single-center design necessitates validation in multicenter cohorts. Furthermore, the limited cohort size and the histological imbalance within the invasive carcinoma group, which was predominantly composed of invasive ductal carcinoma, precluded meaningful stratified analyses according to histological subtype. Therefore, future studies should involve larger patient cohorts with greater clinicopathological diversity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis preliminary study indicates that [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT imaging can rapidly detect tumor changes after two cycles of neoadjuvant therapy, demonstrating its potential for the early prediction of treatment outcome in HER2‑positive breast cancer.\u0026nbsp;Future large-scale prospective studies are necessary to further validate the value of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT in evaluating the efficacy of neoadjuvant therapy in HER2-positive breast cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHuman epidermal growth factor receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNeoadjuvant therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePathological complete response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of the longest diameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSUVmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSUVpeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePeak standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSULmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLean body mass\u0026ndash;normalized maximum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSULmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLean body mass\u0026ndash;normalized mean standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSULpeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLean body mass\u0026ndash;normalized peak standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 affibody-avid tumour volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal lesion activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTumor-to-background ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMetastatic lymph node\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSLNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSentinel lymph node biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAxillary lymph node dissection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eH\u0026amp;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHematoxylin and eosin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the manuscript. Y.S. and R.A. contributed equally as co-first authors. The study concept was proposed by X.Z. Database search was performed by Y.S., X.C., M.W. and J.Z. Analysis and interpretation of data were conducted by R.A., R.Y., X.P., Q.F., and X.C. The manuscript was drafted by X.Z., Y.S., and R.A. Revision of the manuscript was performed by X.Z., Y.Z., Y.S., Y.L., J.H., and N.W.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Hebei Provincial Natural Science Foundation, the Jing-Jin-Ji Special Projects for Basic Research Cooperation (Grant No. H2018206600), and the Hengrui Hebei Collaborative Innovation Program in Medical Science (Grant No. HR202502074).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and materials are available from the corresponding authors upon reasonable request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics committee (Approval No. 2022054). Informed Consent was obtained from all patients participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants in the study were informed and consented to the possibility of research publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63. https://doi.org/10.3322/caac.21834.\u003c/li\u003e\n\u003cli\u003eHayes DF. HER2 and Breast Cancer - A Phenomenal Success Story. 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Intra-image referencing for simplified assessment of HER2-expression in breast cancer metastases using the Affibody molecule ABY-025 with PET and SPECT. Eur J Nucl Med Mol Imaging. 2017;44(8):1337-1346. https://doi.org/10.1007/s00259-017-3650-3.\u003c/li\u003e\n\u003cli\u003eMortimer JE, Bading JR, Park JM, Frankel PH, Carroll MI, Tran TT, et al. Tumor Uptake of 64Cu-DOTA-Trastuzumab in Patients with Metastatic Breast Cancer. Journal of Nuclear Medicine. 2018;59(1):38-43. https://doi.org/10.2967/jnumed.117.193888.\u003c/li\u003e\n\u003cli\u003eHan J Gradishar WJ, Moran MS, Abraham J, Abramson V, Aft R, Agnese D, et al. Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2024;22(5):331\u0026ndash;57. https://doi.org/10.6004/jnccn.2024.0035.\u003c/li\u003e\n\u003cli\u003eD C, Mj PG, Rd G, M P, A G, E de A, et al. 11 years\u0026rsquo; follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive early breast cancer: final analysis of the HERceptin Adjuvant (HERA) trial. Lancet. 2019 16;393(10176):1100. https://doi.org/10.1016/S0140-6736(18)32771-5.\u003c/li\u003e\n\u003cli\u003eGradishar WJ, Moran MS, Abraham J, Abramson V, Aft R, Agnese D, et al. Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2024;22(5):331\u0026ndash;57. https://doi.org/10.6004/jnccn.2024.0035.\u003c/li\u003e\n\u003cli\u003eEisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228\u0026ndash;47. https://doi.org/10.1016/j.ejca.2008.10.026.\u003c/li\u003e\n\u003cli\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathologic complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164-172. https://doi.org/10.1016/S0140-6736(18)32772-7.\u003c/li\u003e\n\u003cli\u003eSymmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25(28):4414\u0026ndash;22. https://doi.org/10.1200/JCO.2007.10.6823.\u003c/li\u003e\n\u003cli\u003eWahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50 Suppl 1(Suppl 1):122S-50S. https://doi.org/10.2967/jnumed.108.057307.\u003c/li\u003e\n\u003cli\u003eMortimer JE, Bading JR, Frankel PH, Carroll MI, Yuan Y, Park JM, et al. Use of 64Cu-DOTA-Trastuzumab PET to Predict Response and Outcome of Patients Receiving Trastuzumab Emtansine for Metastatic Breast Cancer: A Pilot Study. J Nucl Med. 2022;63(8):1145\u0026ndash;8. https://doi.org/10.2967/jnumed.121.262940.\u003c/li\u003e\n\u003cli\u003eYeh R, O\u0026apos;Donoghue JA, Jayaprakasam VS, Mauguen A, Min R, Park S, et al. First-in-Human Evaluation of Site-Specifically Labeled 89Zr-Pertuzumab in Patients with HER2-Positive Breast Cancer. J Nucl Med. 2024;65(3):386-393. https://doi.org/10.2967/jnumed.123.266392.\u003c/li\u003e\n\u003cli\u003eOrlova A, W\u0026aring;llberg H, Stone-Elander S, Tolmachev V. On the selection of a tracer for PET imaging of HER2-expressing tumors: direct comparison of a 124I-labeled affibody molecule and trastuzumab in a murine xenograft model. J Nucl Med. 2009;50(3):417-425. https://doi.org/10.2967/jnumed.108.057919.\u003c/li\u003e\n\u003cli\u003eW\u0026aring;llberg H, Grafstr\u0026ouml;m J, Cheng Q, Lu L, Martinsson Ahlz\u0026eacute;n HS, Sam\u0026eacute;n E, et al. HER2-positive tumors imaged within 1 hour using a site-specifically 11C-labeled Sel-tagged affibody molecule. J Nucl Med. 2012;53 (9):1446-1453. https://doi.org/ 10.2967/jnumed.111.102194.\u003c/li\u003e\n\u003cli\u003eDiwanji D, Ray K, Hylton N. 18F-FDG PET/CT Predicts Response to HER2-directed Neoadjuvant Therapy. Radiol Imaging Cancer. 2021;3(5):e219021. https://doi.org/10.1148/rycan.2021219021.\u003c/li\u003e\n\u003cli\u003eTian F, Shen G, Deng Y, Diao W, Jia Z. The accuracy of 18F-FDG PET/CT in predicting the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis and systematic review. Eur Radiol. 2017;27(11):4786\u0026ndash;96. https://doi.org/10.1007/s00330-017-4831-y.\u003c/li\u003e\n\u003cli\u003eGuo X, Zhou N, Liu J, Ding J, Liu T, Song G, et al. Comparison of an Affibody-based Molecular Probe and 18F-FDG for Detecting HER2-Positive Breast Cancer at PET/CT. Radiology. 2024;311(3):e232209. https://doi.org/10.1148/radiol.232209.\u003c/li\u003e\n\u003cli\u003eChen R, Yang X, Yu X, Zhou X, Ng YL, Zhao H, et al. Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-FAPI-04. Eur J Nucl Med Mol Imaging. 2023;50(3):929\u0026ndash;36. https://doi.org/10.1007/s00259-022-06010-5.\u003c/li\u003e\n\u003cli\u003eTateishi U, Miyake M, Nagaoka T, Terauchi T, Kubota K, Kinoshita T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging--prospective assessment. Radiology. 2012;263(1):53-63. https://doi.org/10.1148/radiol.12111177.\u003c/li\u003e\n\u003cli\u003eFukada I, Araki K, Kobayashi K, Shibayama T, Takahashi S, Gomi N, et al. Pattern of Tumor Shrinkage during Neoadjuvant Chemotherapy Is Associated with Prognosis in Low-Grade Luminal Early Breast Cancer. Radiology. 2018;286(1):49-57. https://doi.org/10.1148/radiol.2017161548.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, HER2, PET/CT, Pathological complete response, Neoadjuvant therapy","lastPublishedDoi":"10.21203/rs.3.rs-8736870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8736870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeoadjuvant therapy (NAT) has become a standard treatment for HER2-positive breast cancer. However, patient responses vary substantially, and reliable methods for early response assessment are still lacking. This study aimed to evaluate the value of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT for the early assessment of treatment outcome by predicting pathological complete response (pCR) in HER2-positive breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e32 of the 54 enrolled patients achieved pCR (59.3%). Following two NAT cycles, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT parameters decreased from baseline in all patients (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Early percentage changes in PET parameters (ΔSUV%, ΔSUL%, ΔTLA%, ΔTBR%) and their absolute values after the second NAT cycle were associated with pCR (r range: -0.658 to -0.273; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05). ΔTBR% demonstrated the best predictive value for pCR (AUC = 0.918), with 93.8% sensitivity and 86.4% specificity at a cutoff of -70.5%. In contrast, tumor size assessment based on RECIST 1.1 showed lower predictive performance, with a sensitivity of 56.3% (18/32) and a specificity of 45.5% (10/22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates the potential of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-HER2 Affibody PET/CT to predict NAT outcome early in HER2-positive breast cancer, which could facilitate subsequent treatment optimization.\u003c/p\u003e","manuscriptTitle":"[68Ga]Ga-HER2 Affibody PET/CT for early prediction of neoadjuvant therapy outcome in HER2-positive breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:54:24","doi":"10.21203/rs.3.rs-8736870/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2026-02-21T02:43:20+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-30T09:22:51+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T06:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T05:46:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2026-01-29T23:14:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7069d3fd-d913-48db-af46-69b8fe1a7e4a","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:13:05+00:00","versionOfRecord":{"articleIdentity":"rs-8736870","link":"https://doi.org/10.1186/s13550-026-01424-w","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2026-04-02 15:58:48","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2026-02-03 15:54:24","video":"","vorDoi":"10.1186/s13550-026-01424-w","vorDoiUrl":"https://doi.org/10.1186/s13550-026-01424-w","workflowStages":[]},"version":"v1","identity":"rs-8736870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8736870","identity":"rs-8736870","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}