Evaluation of ductal carcinoma in situ grade via triple-modal molecular imaging of B7-H3 expression

Bachawal, Sunitha V.; Bean, Gregory R.; Krings, Gregor; Wilson, Katheryne E.

doi:10.1038/s41523-020-0158-y

Cited by 12 publications

(13 citation statements)

References 54 publications

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“…Methods that have been studied to improve inter-observer agreement in DCIS grading include evaluating B7-H3 expression [33], using laboratory-and pathologist-specific feedback reports and e-training [34,35] and applying ad hoc dichotomization of histopathologic features to obtain 'ideal' cut-offs for features used in DCIS grading [36]. Although these studies showed promise in reducing grading variation, overall grading variation remained substantial.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Wetstein

Stathonikos

Pluim

et al. 2021

Laboratory Investigation

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Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen’s kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Wetstein

Stathonikos

Pluim

et al. 2021

Laboratory Investigation

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“…Combined photoacoustic and fluorescence molecular imaging of specific cell surface targets using antibodies conjugated to near-infrared dyes is thus a new and promising approach. Previous reports indicated that combined photoacoustic and fluorescent molecular imaging using an anti-B7-H3 antibody-near infrared dye conjugate could differentiate clinically actionable breast carcinoma and ductal carcinoma in situ from normal mammary tissues in a transgenic murine model of breast cancer (17)(18)(19). Hence, it is hypothesized that the highly specific imaging ability of spectroscopic PAMI combined with a preoperatively administered molecularly tumorspecific contrast agent can be leveraged to improve non-invasive identification of occult LN metastasis in HNSCC patients.…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic Molecular Imaging for the Identification of Lymph Node Metastasis in Head and Neck Cancer Using an Anti-EGFR Antibody–Dye Conjugate

et al. 2020

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The presence of lymph node (LN) metastases is an essential prognostic indicator in patients with head and neck squamous cell carcinoma (HNSCC). This study assessed photoacoustic molecular imaging (PAMI) of the anti-epidermal growth factor receptor (EGFR) antibody (panitumumab) conjugated to a near-infrared fluorescent dye, IRDye800CW (panitumumab-IRDye800CW; pan800) for the identification of occult metastatic LNs in patients with HNSCC (n=7). After in vitro photoacoustic imaging characterization of pan800, PAMI was performed on excised neck specimens of patients infused with pan800 prior to surgery. Freshly obtained neck specimens were imaged with three-dimensional, multiwavelength spectroscopic PAMI (680, 686, 740, 800, 860, 924, and 958 nm wavelengths). Harvested LNs were then imaged with a closed-field near-infrared fluorescence imager and histologically examined by the pathologist to determine their metastatic status. A total of 53 LNs with a maximum diameter of 10 mm were analyzed with photoacoustic and fluorescence imaging, of which four were determined to be metastatic on final histopathology. Photoacoustic signal in the LNs corresponding to accumulated pan800 were spectrally unmixed using a linear least square error classification algorithm. Metastatic LNs had a five-fold higher average thresholded photoacoustic signal intensity corresponding to pan800 compared to benign LNs (2.50 ± 1.09 a.u. vs. 0.53 ± 0.32 a.u., p<0.001). Fluorescence imaging showed that metastatic LNs had a twofold increase in fluorescence signal compared to benign LNs ex vivo (p<0.01, 0.068 ± 0.027 a.u. vs. 0.035 ± 0.018 a.u.). Moreover, the ratio of the average of the highest 10% photoacoustic signal intensity over total average, representative of degree of heterogeneity of pan800 signal in LNs, showed a significant difference between metastatic LNs vs. benign LNs (11.6 ± 13.4 vs. 1.8 ± 0.7, p<0.01) and an area under the receiver operating characteristic (ROC) curve of 0.96 (95% confidence interval; 0.91-1.00). The data indicate that PAMI of IRDye800-labeled tumor-specific antibody may have the potential to identify occult LN metastasis perioperatively in HNSCC patients.

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“…Molecules with high fluorescence QY are good for fluorescence application, whereas NIR dyes with low fluorescence QY can be good candidates for PA contrast agents. Commonly used fluorescence dyes include ICG, 172 , 184 , 185 MB, 186 Evans blue, 187 IRDye800CW, 24 , 188 Alexa Fluor750, 189 , 190 MMPSense 680, 191 NIR caspase-9 probe, 147 800RS 192 etc. Except for ICG, Evans blue, and MB, the other the contrast agents are not FDA-approved for human use; hence, they can be evaluated only in phantoms or animal models.…”

Section: Photoacoustic Imaging With Exogeneous Probesmentioning

confidence: 99%

Looking deep inside tissue with photoacoustic molecular probes: a review

2022

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. Significance: Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background. Aim: We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents. Approach: First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications. Results: Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy. Conclusions: The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.

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Evaluation of ductal carcinoma in situ grade via triple-modal molecular imaging of B7-H3 expression

Cited by 12 publications

References 54 publications

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Photoacoustic Molecular Imaging for the Identification of Lymph Node Metastasis in Head and Neck Cancer Using an Anti-EGFR Antibody–Dye Conjugate

Looking deep inside tissue with photoacoustic molecular probes: a review

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