Skip to main content
Download PDF

Detection of breast cancer lesions using APT weighted MRI: a systematic review

Journal of Translational Medicine volume 23, Article number: 141 (2025) Cite this article

Abstract

Amide proton transfer (APT) is a novel magnetic resonance imaging (MRI) technique that has shown promising ability to study cancers. This paper systematically reviewed the literature on the use of APT MRI in the prognosis of breast cancer. A literature search was conducted on Pubmed and Embase and a total of 14 articles comprising 775 patients were included in the review. APT MRI had the ability to distinguish between benign and malignant lesions with an AUC as high as 0.959. There is a positive correlation between APT signal intensity and tumor grade/stage as well as Ki-67, whereas no correlation was found with ER/PR/Her-2 receptor status. There was a greater decrease in APT signal intensity after neoadjuvant chemotherapy (NAC) in responders compared to non-responders, suggesting that APT MRI may serve as a valuable supplemental tool in the early identification of chemotherapy response. APT has the potential to complement with other imaging methods in the diagnosis, prognosis, treatment monitoring, and management of breast cancer. Additional studies and standardization of APT acquisition methods are needed.

Background

Breast cancer is the leading cancer diagnosis and second leading cause of cancer deaths in women in the United States [1]. It is a highly heterogeneous disease whose diagnosis, treatment, and prognosis are heavily influenced by various factors including receptor status (such as estrogen (ER), progesterone (PR) receptor), pathologic grade, and even Ki-67 level [2, 3].

Magnetic resonance imaging (MRI) is the standard imaging method for diagnosis and monitoring of disease progression and treatment outcomes because it is non-invasive, offers excellent soft tissue contrast, and excellent spatiotemporal resolution [4,5,6]. Common breast MRI methods include Dynamic Contrast-Enhanced MRI (DCE-MRI), Diffusion-Weighted Imaging (DWI), T2-Weighted Imaging (T2WI), and less commonly used methods such as MR elastography and MR spectroscopy. However, contrast agents are frequently required in the prior approaches which may present renal complications and contrast deposition in tissue [7, 8].

Chemical exchange saturation transfer (CEST) has been introduced as a novel contrast method which measures the exchange of protons between amide, amine, hydroxyl, and water groups [9]. Of note, it does not require external contrast agents, which may expand its usefulness among patients with renal complications. While there are various approaches to CEST that measure different functional groups, a specific subset of CEST measures the amide proton transfer (APT) at 3.5ppm away from the resonance frequency of water and has been widely investigated in various cancers [10,11,12]. Amide proton transfer weighted imaging is sensitive to endogenous mobile proteins and peptides in tissue as opposed to the hydrogen atoms in free water which is measured in many other MR approaches. The principles and theories of CEST and APT weighted imaging have been reviewed in numerous articles [13,14,15,16,17,18]. In brief, the APT signal is observed as a decrease in the water signal caused by the exchange of water protons with tagged amide protons in mobile proteins. This provides indirect information about specific proteins through the water signal typically analyzed in imaging. The tagging is achieved by applying selective radiofrequency (RF) waves at the specific frequency of amide protons, approximately + 3.5 ppm downfield of the water signal. The data is analyzed using a Z-spectrum, which shows how the water signal changes with and without RF saturation. However, effects such as direct water saturation (DS) and magnetization transfer contrast (MTC) can interfere with the measurements. These effects are separated using asymmetry analysis, which calculates the difference in the Z-spectrum at + 3.5 ppm (amide protons) and − 3.5 ppm (other effects), reported as MTRasym. A schematic of APT-weighted imaging is provided in Fig. 1.

Since the 2000s, researchers have investigated the potential for APT imaging in cancer diagnosis. In preclinical tumor models, increases in cytosolic protein concentrations have been found and correlated to increases in APT signal intensity [19, 20]. A further study has found that approximately 66% of an APT signal change in tumors can be attributed to protein concentration differences as malignant tumors typically have increased free protein due to angiogenesis and hypercellularity [21]. This theory can be applied clinically as APT has been shown to be able to distinguish malignant from benign lesions, predict certain histologic grades, and correlate positively with response to neoadjuvant chemotherapy (NAC) response. APT weighted imaging applications have predominantly been investigated in brain tumors [22]. It has also been investigated in other cancers including salivary gland cancers, head and neck cancers, and even secondary metastases [23,24,25]. Its use in clinical practice is steadily increasing, which has led to the development of consensus recommendations for standardization across different centers and MRI vendors for the study of brain tumors [22].

In recent years, a few studies have investigated the use of APT MRI in breast cancer. Like studies in other cancers, they have also centered around distinguishing malignant from benign lesions, evaluating tumor characteristics, and predicting NAC response. Comprehensive review consolidating the body of research on APT MRI application to breast cancer is lacking. We thus conducted this systematic review to assess the diagnostic and prognostic use of APT in breast cancer and its future clinical applications.

Fig. 1
figure 1

APT weighted imaging principle. A radiofrequency is applied at the specific frequency of amide protons 3.5ppm downfield the water signal. The exchange of amide protons with free water protons causes a change in the water signal. Asymmetry analysis around ± 3.5ppm is analyzed as the APT weighted signal and reported as MTRasym

Full size image

Methods

Eligibility criteria

Using Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), we conducted a systematic review of studies that analyzed the use of APT in identifying breast cancer lesions (Fig. 2). A search was performed on PubMed and Embase databases and articles up to July 8 2024 were analyzed. The search parameters can be found in the supplemental information. Cohort studies and case series were included in the analyses. The following study types were excluded: (1) case reports, review papers, and conference abstracts; (2) papers not written in English; (3) protocol papers, letters to the editor, preprint papers, and healthcare provider surveys without data; and (4) papers that did not use APT as a data metric.

The title and abstract of papers after the initial search were assessed by two independent reviewers, M.B and R.L, and only studies approved by both reviewers were included. Disputes regarding the inclusion of a paper were decided by a third reviewer, T.D.

Data collection and analysis

Study characteristics including study design, origin, sample size, and lesion characteristics as well as outcomes such as APT signal intensity and histopathological data were collected manually by two independent reviewers. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of each study and the risk of bias. The results can be found in the supplemental information.

Fig. 2
figure 2

PRISMA diagram demonstrating the study inclusion criteria

Full size image

Results

Study characteristics

Thirty-nine unique papers were identified through PubMed and Embase searches. After review of abstracts, 25 studies were excluded for the following reasons: article was not written in English (2), article was not cohort or case series study (5), study did not use APT as a metric in the study of human breast cancer (18) (Fig. 2). The study characteristics and main findings of the 14 included papers are shown in Table 1. In summary, there were 10 prospective cohort studies, 3 retrospective cohort studies, and 1 case series. In total, 775 patients underwent an APT MRI scan, of which there were 568 malignant lesions and 214 benign lesions. All malignant lesions were verified by histology. Additional information regarding the average size of the lesions and histopathological data including ER status, PR status, HER2 status, Ki67%, and histological grade can be found in the Supplemental Data (Supplemental Table 1).

Table 1 Paper characteristics
Full size table

Distinguishing benign vs. malignant lesions

Eight studies reported the APT signal values for benign and malignant lesions (Table 2). Four studies reported a significantly higher APT signal in malignant lesions, 3 studies reported significantly higher APT signal in the benign lesions, and 1 study only reported APT signal for only malignant lesions. It should be noted that 2 of the studies that reported a higher APT signal in benign lesions (Meng 1 [29] and Meng 2 [35]) may have overlapping patient cohorts. Seven studies reported an APT signal cut-off value for differentiating benign and malignant lesions. Of the papers that reported a higher APT signal for malignant lesions, Zhang et al. [30] reported the highest AUC of 0.959. Of the papers that reported a higher APT signal for benign lesions, Li et al. [32] reported the highest AUC of 0.816.

Table 2 APT values of benign vs. malignant lesions, sensitivity, specificity, AUC
Full size table

Determination of histopathological characteristics

Table 3 reports the correlation of APT signal intensity with various histopathological characteristics. Six studies evaluated the correlation between APT signal intensity and tumor grade with 3 studies reporting a positive correlation higher tumor grade and 3 studies reporting no correlation. One study reported a positive correlation between tumor stage and signal intensity. Seven studies evaluated the correlation between APT signal intensity and ER status with 1 study reporting a positive correlation with ER-negative status and 5 studies reporting no correlation. Seven studies evaluated the correlation between APT signal intensity and either PR or HER-2 status with all 7 studies reporting no correlation. Nine studies evaluated the correlation between APT signal intensity and Ki-67 expression and 3 reported a positive correlation with higher Ki-67 expression and 7 studies reported no correlation. One paper (Kamitami [27]) found that triple negative tumors had higher APT values than other subtypes. Specific APT values can be found in Supplemental Tables 2 and 3.

Table 3 APT correlation with histopathological characteristics
Full size table

Prediction of response to neoadjuvant chemotherapy

Four papers investigated changes in APT signal intensities before and after neoadjuvant chemotherapy (NAC). A case series of 3 patients found an increase in APT signal in a non-responder and partial responder and a decrease in signal in the 1 complete responder after one cycle of chemotherapy (Dula et al. [36]). Likewise, a study of 9 patients (10 lesions) showed that responders tend to have a lower APT signal after one cycle of chemotherapy compared to non-responders (Krikken E-1 [38]). Another study found that while APT signal intensity decreased in both responders and non-responders after 2 cycles of NAC, responders (N = 14) had a greater decrease in APT after NAC compared to non-responders (N = 36) (Zhang N-2 [34]). They also reported that pre-NAC APT signal intensity could predict response to NAC with an AUC of 0.690, while APT signal data pre- and post-NAC could predict response to NAC with an AUC of 0.879. Moreover, if tumor diameter data is included, the AUC increases to 0.903. Another study showed a significant decrease in APT signal at 0.9T but not 2.0T after the first two cycles of chemotherapy compared to baseline in the responders (N = 26), however, no difference in signal intensity in either strength was seen after the 4th cycle of chemotherapy compared to baseline [31]. No difference in signal intensity was noted between the pathological responders and non-responders at any point in time.

Discussion

We review the use of APT to distinguish benign from malignant breast cancer lesions, characterize breast cancer lesions and predict breast cancer response to NAC. Our main findings are: (i) APT can distinguish benign from malignant breast cancer lesions with an AUC as high as 0.959, (ii) there is evidence that APT signal is positively correlated with a higher tumor grade/stage, although there are studies that report otherwise, (iii) there is no evidence to suggest a correlation between APT signal and ER/PR/Her-2 receptor status, (iv) there is weak evidence of a positive correlation between APT signal and higher Ki-67 expression, and (v) responders to NAC show a greater decrease in APT signal intensity after NAC compared to non-responders, although there are studies that report otherwise. Overall, these results suggest that APT has the potential to be used as a valuable imaging biomarker to differentiate benign from malignant breast cancer lesions and identify pathological responders to NAC.

Malignant vs. Benign

Malignant tumors are recognized as having a higher APT signal intensity compared to benign tumors, which is consistent with prior studies of brain, gynecological, bladder, head and neck, and parotid gland tumors [23, 24, 40,41,42,43]. This heightened signal is thought to be due to a number of properties inherent to malignant tumors such as hypercellularity, fast cell turnover, and increased metabolic activity. Cells with faster turnover and increased metabolic activity have higher intracellular protein concentrations, amplifying the APT signal [13, 20, 44]. Likewise, the densely packed, hypercellular nature of malignant tumors further increases the local concentration of proteins, boosting signal intensity. Angiogenesis by malignant tumors may contribute to a higher signal too as the blood contains high concentrations of hemoglobin and albumin [13].

Four studies reported a higher APT signal intensity in malignant lesions compared to benign lesions, consistent with existing literature [26, 28, 30, 33]. However, the other 3 studies reported a higher APT signal in benign lesions [29, 32, 35]. One proposed mechanism for this finding is that physiologic breast secretions are rich in protein, and if these secretions are limited as in the case of an underlying malignancy, the number of proteins in the malignant lesions should decrease, as should the APT signal intensity [29, 35]. However, if this proposed mechanism were true, then high-grade malignant lesions would have a lower intensity than low grade lesions which is not observed in the aforementioned studies. Moreover, this hypothesis is not consistent with malignancies of other secretory organs, such as the parotid glands [23, 45]. Furthermore, malignant breast cancer cells are thought to secrete more proteins compared to benign cells in order to maintain a tumorigenic state [46]. Another possible reason for a higher APT signal in benign lesions could be due to variations in protein secretions between breast cancer subtypes. However, since all the studies have a heterogeneous sample of both benign and malignant breast cancer lesions, it is difficult to make conclusions regarding this hypothesis. Nonetheless, we implore future studies to report the APT signal intensity of each breast cancer subtype.

APT correlation with histologic grade and Ki-67

Tumor histopathological characteristics are crucial in developing a treatment plan and determining prognosis for breast cancers but require an invasive biopsy. APT imaging may provide a non-invasive method of garnering histopathological information. In this review, all studies that investigated a correlation between APT signal intensity and Ki-67 found a positive correlation, though only two studies reached statistical significance. This is notable as Ki-67 has been a well-established marker of tumor proliferation and is utilized in therapeutic decision making [47]. Furthermore, prior studies have found that patients with low Ki-67 had significantly greater rates of survival and lower rates of metastases, though the full validity of Ki-67 as a robust prognostic marker is an ongoing area of research [2, 48]. It should be noted that different groups utilized different cutoffs for defining a high Ki-67. The two groups which found significant correlations utilized cutoffs of 20% and 30%, respectively, but the other groups did not find any correlations using a cut-off of 14%. At present, there is no well-established, clinically relevant cutoff for high and low Ki-67 statuses outside of levels less than 5% or greater than 30% [48]. Similarly, tumor grade plays an important prognostic role as higher grade tumors have worse prognosis than lower grade tumors [49]. All studies reported a higher signal intensity for higher graded/staged tumors with most of the studies reaching statistical significance.

Ultimately, tumor characteristics with worse prognosis, such as a high-Ki67, high histologic grade, and triple negative tumors appear to have higher APT signal intensities. Although APT imaging cannot provide definitive histopathological data, the presence of a high signal may be an early, non-invasive indication of a more severe malignancy which may aid clinicians in taking a more aggressive therapeutic approach. Future studies analyzing the ability of APT imaging to categorize lesions by tumor grade may be useful.

APT signal of responders and non-responders to neoadjuvant chemotherapy

Early evaluation and prediction of the effectiveness of NAC is crucial in cancer treatment as it allows refinement of chemotherapy treatment prior to surgical intervention. One study in this review found a lower pretreatment ATP signal intensity to be a predictor of response to NAC, which is consistent with existing literature of rectal, cervical, and nasopharyngeal cancers [12, 50, 51]. The other studies found a difference between baseline APT signal intensity, which could be due to the smaller sample size in these studies. Most studies in this review suggest that responders to chemotherapy have a greater decrease in APT signal intensity than non-responders, though not all results reached statistical significance. This is consistent with a study of nasopharyngeal carcinoma, and other existing literature that suggests a decrease in APT is expected in responders approximately 6–7 days post-chemotherapy due to tumor necrosis [52, 53].

The one study that generated a predictive model to differentiate responders from non-responders found a high predictive value when using pre- and post-NAC APT signal intensity data [34]. The inclusion of tumor diameter data further increased the predictive value, suggesting that the use of APT imaging in conjunction with additional tumor characteristics may be a powerful method of differentiating responders from non-responders in the early stages of chemotherapy. The prediction using only pre-NAC data was not nearly as successful, despite other predictive models of rectal and cervical cancers using only pre-NAC APT signal intensity data showing promising results [12, 50]. The implementation of additional tumor characteristics, such as tumor diameter, to pre-NAC models in breast cancer has yet to be explored and could provide a reliable prediction even prior to the induction of chemotherapy.

Comparison with other methods

Breast MRI has transitioned from relying mainly on contrast-enhanced imaging to a more multiparametric approach. Today, it commonly includes with and without fat-suppression, non-fat suppressed T1-weighted, and diffusion-weighted imaging as standard components. The multiparametric approach has allowed for increased diagnostic power of malignant lesions, especially in incidental lesion findings. However, the foundation of any breast MRI protocol remains the dynamic contrast-enhanced T1-weighted sequence [54, 55]. The T1-contrast enhanced sequence’s primary limitation remains the need for invasive intravenous contrast which may carry long term effects and is not suitable for all patients [54]. A novelty of the APT method lies in its ability to measure signals based on cytosolic protein changes which is distinct from other common contrast methods. Some studies have compared APT to other MRI methods in their ability to distinguish malignancy of lesions. Four groups reported that APT was at least as efficient in distinguishing malignancy when compared to other methods including DWI, DCE, intravoxel incoherent motion imaging (IVIM), and diffusion kurtosis imaging (DKI) [26, 30, 32, 33]. Of these groups, the AUC for APTw imaging ranged from 0.816 to 0.959. This was compared to AUCs of DCE ranging from 0.745 to 0.976 and DWI ranging from 0.878 to 0.897. There was one group whose analysis showed that APT was slightly inferior to DKI [35]. Interestingly, some groups also used APT as a supplement to other diagnostic methods and found that DCE combined with APT could yield a higher AUC for distinguishing malignant from benign lesions [26, 32, 35]. These results suggest that APT can be a supplemental MRI method in the multiparametric approach to amplify the diagnostic strength of traditional MRI contrast approaches or as an alternative to those with renal complications and pregnant patients.

Limitations

There are several limitations in this study. First, variation in image processing and data analysis make interstudy comparison of APT signal intensity difficult. For instance, fat suppression is necessary given the high presence of adipose tissue in the breast. Without correction for the fat signal, there may be incorrect normalization of the APT signal or observation of pseudo-signals [56]. Studies used different techniques including short T1 inversion recovery, spectral pre-saturation with inversion recovery, water excitation, or simply placing the region of interest over the tumor to eliminate adipose signals. Furthermore, whereas some groups reported the APT signal intensity by conducting MTR asymmetry analysis, other studies conducted an additional Lorenztian fit analysis before reporting the signal intensity. Some studies did not directly report their method of calculating APT signal intensity.

APT signal intensity of breast cancer subtypes was not reported which could present potential confounders. For example, the secretory nature of the mucinous adenocarcinoma subtype may positively skew the overall malignant breast cancer APT signal intensity. Future studies should report the APT signal intensity of the breast cancer subtypes.

Lastly, unlike imaging of other organs such as the brain, breast cancer imaging is more susceptible to artifacts from respiratory and cardiac motion [57]. There is also much greater variability in tissue density and vascularity in the breast compared to the brain.

Of note, the literature regarding APT weighted detection of breast cancer lesions is still fairly limited, with fourteen papers reported in this review. The sample size of each study was also limited in size. When combined with the lack of acquisition and signal processing standardization between studies, this makes the current literature insufficient to make any confirmatory or statistically significant conclusions through meta analyses.

Conclusion

APT signal can distinguish between malignant and benign breast cancer. More proliferative cancer with worse prognosis generally has higher APT signal intensities. There is some evidence that APT signal is associated with histologic grade, Ki-67 and pathological complete response to NAC. APT has the potential to complement with other imaging methods in the diagnosis, prognosis, and management of breast cancer. Additional studies and standardization of APT acquisition methods are needed.

References

  1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12–49.

    Article  PubMed  Google Scholar 

  2. Soliman NA, Yussif SM. Ki-67 as a prognostic marker according to breast cancer molecular subtype. Cancer Biol Med. 2016;13:496–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hwang K-T, Kim J, Jung J, Chang JH, Chai YJ, Oh SW, Oh S, Kim YA, Park SB, Hwang KR. Impact of breast Cancer subtypes on prognosis of women with operable invasive breast Cancer: a Population-based study using SEER database. Clin Cancer Res. 2019;25:1970–9.

    Article  PubMed  Google Scholar 

  4. Morrow M, Waters J, Morris E. MRI for breast cancer screening, diagnosis, and treatment. Lancet. 2011;378:1804–11.

    Article  PubMed  Google Scholar 

  5. Bakker MF, de Lange SV, Pijnappel RM, et al. Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med. 2019;381:2091–102.

    Article  PubMed  Google Scholar 

  6. Rizzo V, Moffa G, Kripa E, Caramanico C, Pediconi F, Galati F. Preoperative staging in breast Cancer: Intraindividual comparison of Unenhanced MRI Combined with Digital breast tomosynthesis and dynamic contrast Enhanced-MRI. Front Oncol. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fonc.2021.661945.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Weinreb JC, Rodby RA, Yee J, Wang CL, Fine D, McDonald RJ, Perazella MA, Dillman JR, Davenport MS. Use of Intravenous Gadolinium-based Contrast Media in patients with kidney disease: Consensus statements from the American College of Radiology and the National Kidney Foundation. Radiology. 2021;298:28–35.

    Article  PubMed  Google Scholar 

  8. Kanda T, Fukusato T, Matsuda M, Toyoda K, Oba H, Kotoku J, Haruyama T, Kitajima K, Furui S. Gadolinium-based contrast Agent accumulates in the Brain even in subjects without severe renal dysfunction: evaluation of autopsy brain specimens with inductively coupled plasma Mass Spectroscopy. Radiology. 2015;276:228–32.

    Article  PubMed  Google Scholar 

  9. Dou W, Lin C-YE, Ding H, Shen Y, Dou C, Qian L, Wen B, Wu B. Chemical exchange saturation transfer magnetic resonance imaging and its main and potential applications in pre-clinical and clinical studies. Quant Imaging Med Surg. 2019;9:1747766–1741766.

    Article  Google Scholar 

  10. Choi SH. Can Amide Proton transfer MRI distinguish Benign and Malignant Head and Neck tumors? Radiology. 2018;288:791–2.

    Article  PubMed  Google Scholar 

  11. Wang HJ, Cai Q, Huang YP, Li MQ, Wen ZH, Lin YY, Ouyang LY, Qian L, Guo Y. Amide Proton transfer-weighted MRI in Predicting histologic Grade of bladder Cancer. Radiology. 2022;305:127–34.

    Article  PubMed  Google Scholar 

  12. Nishie A, Asayama Y, Ishigami K, et al. Amide Proton transfer imaging to predict tumor response to neoadjuvant chemotherapy in locally advanced rectal cancer. J Gastroenterol Hepatol. 2019;34:140–6.

    Article  CAS  PubMed  Google Scholar 

  13. Zhou J, Heo H-Y, Knutsson L, van Zijl PCM, Jiang S. APT-weighted MRI: techniques, current neuro applications, and challenging issues. J Magn Reson Imaging JMRI. 2019;50:347–64.

    Article  PubMed  Google Scholar 

  14. Zhou J, van Zijl PCM. Chemical exchange saturation transfer imaging and spectroscopy. Prog Nucl Magn Reson Spectrosc. 2006;48:109–36.

    Article  CAS  Google Scholar 

  15. Vinogradov E, Sherry AD, Lenkinski RE. CEST: from basic principles to applications, challenges and opportunities. J Magn Reson. 2013;229:155–72.

    Article  CAS  PubMed  Google Scholar 

  16. van Zijl PCM, Yadav NN. Chemical exchange saturation transfer (CEST): what is in a name and what isn’t? Magn Reson Med. 2011;65:927–48.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kamimura K, Nakajo M, Yoneyama T, Takumi K, Kumagae Y, Fukukura Y, Yoshiura T. Amide Proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions. Jpn J Radiol. 2019;37:109–16.

    Article  PubMed  Google Scholar 

  18. Gao T, Zou C, Li Y, Jiang Z, Tang X, Song X. A brief history and future prospects of CEST MRI in clinical non-brain Tumor Imaging. Int J Mol Sci. 2021;22:11559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhou J, Lal B, Wilson DA, Laterra J, van Zijl PCM. Amide Proton transfer (APT) contrast for imaging of brain tumors. Magn Reson Med. 2003;50:1120–6.

    Article  PubMed  Google Scholar 

  20. Yan K, Fu Z, Yang C, et al. Assessing Amide Proton transfer (APT) MRI contrast origins in 9 L gliosarcoma in the rat brain using proteomic analysis. Mol Imaging Biol MIB off Publ Acad Mol Imaging. 2015;17:479–87.

    CAS  Google Scholar 

  21. Ray KJ, Simard MA, Larkin JR, Coates J, Kinchesh P, Smart SC, Higgins GS, Chappell M, Sibson N. Tumour pH and protein concentration contribute to the signal of amide proton transfer magnetic resonance imaging. Cancer Res. 2019;79:1343–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou J, Zaiss M, Knutsson L, et al. Review and consensus recommendations on clinical APT -weighted imaging approaches at 3T: application to brain tumors. Magn Reson Med. 2022;88:546–74.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Takumi K, Nagano H, Kikuno H, Kumagae Y, Fukukura Y, Yoshiura T. Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach. Sci Rep. 2021;11:2780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yu L, Li C, Luo X, Zhou J, Zhang C, Zhang Y, Chen M. Differentiation of malignant and Benign Head and Neck Tumors with Amide Proton transfer-weighted MR Imaging. Mol Imaging Biol. 2019;21:348–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sartoretti E, Sartoretti T, Wyss M, Reischauer C, van Smoorenburg L, Binkert CA, Sartoretti-Schefer S, Mannil M. Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases. Sci Rep. 2021;11:5506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhuang L, Lian C, Wang Z, Zhang X, Wu Z, Huang R. Breast-lesion assessment using amide proton transfer-weighted imaging and dynamic contrast-enhanced MR imaging. Radiol Oncol. 2023;57:446–54.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kamitani T, Sagiyama K, Yamasaki Y, Hino T, Wada T, Kubo M, Akiyoshi S, Yamamoto H, Yabuuchi H, Ishigami K. Amide Proton transfer (APT) imaging of breast cancers and its correlation with biological status. Clin Imaging. 2023;96:38–43.

    Article  PubMed  Google Scholar 

  28. Liu Z, Wen J, Wang M, et al. Breast Amide Proton Transfer Imaging at 3 T: diagnostic performance and Association with pathologic characteristics. J Magn Reson Imaging. 2023;57:824–33.

    Article  PubMed  Google Scholar 

  29. Meng N, Wang X, Sun J, Han D, Bai Y, Wei W, Wang Z, Jia F, Wang K, Wang M. A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer. Eur Radiol. 2021;31:1707–17.

    Article  CAS  PubMed  Google Scholar 

  30. Zhang N, Kang J, Wang H, et al. Differentiation of fibroadenomas versus malignant breast tumors utilizing three-dimensional amide proton transfer weighted magnetic resonance imaging. Clin Imaging. 2022;81:15–23.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang S, Rauch GM, Adrada BE, et al. Assessment of early response to Neoadjuvant systemic therapy in Triple-negative breast Cancer using Amide Proton transfer–weighted Chemical Exchange Saturation transfer MRI: a pilot study. Radiol Imaging Cancer. 2021;3:e200155.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Li Y, Zhang Y, Tian L, Li J, Li H, Wang X, Wang C. 3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study. Eur Radiol Exp. 2024;8:41.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yu T, Li L, Shi J, et al. Predicting histopathological types and molecular subtype of breast tumors: a comparative study using amide proton transfer-weighted imaging, intravoxel incoherent motion and diffusion kurtosis imaging. Magn Reson Imaging. 2024;105:37–45.

    Article  CAS  PubMed  Google Scholar 

  34. Zhang N, Song Q, Liang H et al. Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI. Front Med. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fmed.2024.1295478

  35. Meng N, Wang X-J, Sun J, Huang L, Wang Z, Wang K-Y, Wang J, Han D-M, Wang M-Y. Comparative study of Amide Proton Transfer-Weighted Imaging and Intravoxel Incoherent Motion Imaging in breast Cancer diagnosis and evaluation. J Magn Reson Imaging. 2020;52:1175–86.

    Article  PubMed  Google Scholar 

  36. Dula AN, Arlinghaus LR, Dortch RD, Dewey BE, Whisenant JG, Ayers GD, Yankeelov TE, Smith SA. Amide Proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response. Magn Reson Med. 2013;70:216–24.

    Article  CAS  PubMed  Google Scholar 

  37. Loi L, Zimmermann F, Goerke S, et al. Relaxation-compensated CEST (chemical exchange saturation transfer) imaging in breast cancer diagnostics at 7T. Eur J Radiol. 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejrad.2020.109068.

    Article  PubMed  Google Scholar 

  38. Krikken E, Khlebnikov V, Zaiss M, Jibodh RA, van Diest PJ, Luijten PR, Klomp DWJ, van Laarhoven HWM, Wijnen JP. Amide chemical exchange saturation transfer at 7 T: a possible biomarker for detecting early response to neoadjuvant chemotherapy in breast cancer patients. Breast Cancer Res. 2018;20:51.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Krikken E, van der Kemp WJM, Khlebnikov V, van Dalen T, Los M, van Laarhoven HWM, Luijten PR, van den Bosch MAAJ, Klomp DWJ, Wijnen JP. Contradiction between amide-CEST signal and pH in breast cancer explained with metabolic MRI. NMR Biomed. 2019;32:e4110.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Li Y, Lin C-Y, Qi Y-F, et al. Non-invasive differentiation of endometrial adenocarcinoma from Benign lesions in the Uterus by utilization of Amide Proton transfer-weighted MRI. Mol Imaging Biol. 2021;23:446–55.

    Article  CAS  PubMed  Google Scholar 

  41. Koike H, Morikawa M, Ishimaru H, Ideguchi R, Uetani M, Miyoshi M. Amide Proton transfer–Chemical Exchange Saturation transfer imaging of intracranial brain tumors and tumor-like lesions: our experience and a review. Diagnostics. 2023;13:914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li J-L, Xu Y, Xiang Y-S, Wu P, Shen A-J, Wang P-J, Wang F. The Value of Amide Proton Transfer MRI in the diagnosis of malignant and benign urinary bladder lesions: comparison with diffusion-weighted imaging. J Magn Reson Imaging. 2024;60:1124–33.

    Article  PubMed  Google Scholar 

  43. Guo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, Zhuang Z, Zhu X. Amide Proton transfer could provide more accurate lesion characterization in the Transition Zone of the prostate. J Magn Reson Imaging. 2022;56:1311–9.

    Article  PubMed  Google Scholar 

  44. Lee D-H, Heo H-Y, Zhang K, Zhang Y, Jiang S, Zhao X, Zhou J. Quantitative assessment of the effects of water proton concentration and water T1 changes on amide proton transfer (APT) and nuclear overhauser enhancement (NOE) MRI: the origin of the APT imaging signal in brain tumor. Magn Reson Med. 2017;77:855–63.

    Article  CAS  PubMed  Google Scholar 

  45. Bae YJ, Choi BS, Jeong W-J, Jung YH, Park JH, Sunwoo L, Jung C, Kim JH. Amide Proton transfer-weighted MRI in the diagnosis of major salivary gland tumors. Sci Rep. 2019;9:8349.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zahari S, Syafruddin SE, Mohtar MA. Impact of the Cancer Cell Secretome in driving breast Cancer progression. Cancers. 2023;15:2653.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Davey MG, Hynes SO, Kerin MJ, Miller N, Lowery AJ. Ki-67 as a prognostic biomarker in invasive breast Cancer. Cancers. 2021;13:4455.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Nielsen TO, Leung SCY, Rimm DL, et al. Assessment of Ki67 in breast Cancer: updated recommendations from the International Ki67 in breast Cancer Working Group. JNCI J Natl Cancer Inst. 2021;113:808–19.

    Article  PubMed  Google Scholar 

  49. Schwartz AM, Henson DE, Chen D, Rajamarthandan S. Histologic Grade remains a prognostic factor for breast Cancer regardless of the number of positive lymph nodes and tumor size: a study of 161 708 cases of breast Cancer from the SEER Program. Arch Pathol Lab Med. 2014;138:1048–52.

    Article  PubMed  Google Scholar 

  50. Deng X, Liu M, Zhou Q, Zhao X, Li M, Zhang J, Shen H, Lan X, Zhang X, Zhang J. Predicting treatment response to concurrent chemoradiotherapy in squamous cell carcinoma of the cervix using amide proton transfer imaging and intravoxel incoherent motion imaging. Diagn Interv Imaging. 2022;103:618–24.

    Article  PubMed  Google Scholar 

  51. Qamar S, King AD, Ai Q-YH, et al. Pre-treatment amide proton transfer imaging predicts treatment outcome in nasopharyngeal carcinoma. Eur Radiol. 2020;30:6339–47.

    Article  CAS  PubMed  Google Scholar 

  52. Qamar S, King AD, Ai Q-Y, et al. Amide Proton transfer MRI detects early changes in nasopharyngeal carcinoma: providing a potential imaging marker for treatment response. Eur Arch Otorhinolaryngol. 2019;276:505–12.

    Article  PubMed  Google Scholar 

  53. Wang X, Ishimatsu K, Li J, Wen X, Ou W, Anwar A, Chaudhary J, Takahashi M, Sherry AD, Corbin IR. APT imaging of hepatocellular carcinoma signals an effective therapeutic response in advance of tumor shrinkage. Hepatic Oncol. 2024;11:2389031.

    Article  Google Scholar 

  54. American College of Radiology. ACR Practice parameter for the performance of Contrast-Enhanced Magnetic Resonance Imaging (MRI) of the breast. American College of Radiology; 2021.

  55. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019;292:520–36.

    Article  PubMed  Google Scholar 

  56. Zhang S, Keupp J, Wang X, Dimitrov I, Madhuranthakam AJ, Lenkinski RE, Vinogradov E. Z-spectrum appearance and interpretation in the presence of fat: influence of acquisition parameters. Magn Reson Med. 2018;79:2731–7.

    Article  PubMed  Google Scholar 

  57. Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, Sieberg R, Hylton N, Joe BN, Lee AY. Feasibility of simulated contrast-enhanced breast MRI for Imaging Malignant masses using deep learning. Radiology. 2023;306:e213199.

    Article  PubMed  Google Scholar 

Download references

Funding

None.

Author information

Author notes

  1. Ryan C. Lee and Montek Singh Boparai contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA

    Ryan C. Lee, Montek Singh Boparai & Tim Q. Duong

Authors
  1. Ryan C. Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Montek Singh Boparai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tim Q. Duong
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

RCL and MSB performed literature search, analyzed data, and wrote paper. TQD edited paper and supervised.

Corresponding author

Correspondence to Tim Q. Duong.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, R.C., Boparai, M.S. & Duong, T.Q. Detection of breast cancer lesions using APT weighted MRI: a systematic review. J Transl Med 23, 141 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-025-06153-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-025-06153-7

Keywords

Journal of Translational Medicine

ISSN: 1479-5876

Contact us