Accuracy of Post–Neoadjuvant Chemotherapy Image-Guided Breast Biopsy to Predict Residual Cancer

Tasoulis, Marios Konstantinos; Lee, Han‐Byoel; Yang, Wei Tse; Pope, Romney; Krishnamurthy, Savitri; Kim, Soo Yeon; Cho, Nariya; Teoh, Victoria; Rauch, Gaiane M.; Smith, Benjamin D.; Valero, Vicente; Mohammed, Kabir; Han, Wonshik; MacNeill, Fiona; Kuerer, Henry Mark

doi:10.1001/jamasurg.2020.4103

Cited by 64 publications

(44 citation statements)

References 23 publications

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“…In contrast, tissue-based biomarkers are characterized by the need to obtain new biopsies, analytical difficulties such as non-representative material or poor cellularity, and interobserver variability, although standardization improves yield and their performance. 38 Literature on TK1 activity suggests that it may have diverse interpretations: proliferation, monitoring during treatment, response to therapy or even resistance to cyclin-dependent kinase 4/6 inhibitors, 39 , 40 , 41 depending on the clinical setting in which it is assessed. No available data, however, answer the question whether changes in proliferation during NACT can exclusively explain the prognostic value of TK1 and its kinetics.…”

Section: Discussionmentioning

confidence: 99%

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

et al. 2021

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Background Emerging data support the use of thymidine kinase 1 (TK1) activity as a prognostic marker and for monitoring of response in breast cancer (BC). The long-term prognostic value of TK1 kinetics during neoadjuvant chemotherapy is unclear, which this study aimed to elucidate. Methods Material from patients enrolled to the single-arm prospective PROMIX trial of neoadjuvant epirubicin, docetaxel and bevacizumab for early BC was used. Ki67 in baseline biopsies was assessed both centrally and by automated digital imaging analysis. TK1 activity was measured from blood samples obtained at baseline and following two cycles of chemotherapy. The associations of TK1 and its kinetics as well as Ki67 with event-free survival and overall survival (OS) were evaluated using multivariable Cox regression models. Results Central Ki67 counting had excellent correlation with the results of digital image analysis ( r = 0.814), but not with the diagnostic samples ( r = 0.234), while it was independently prognostic for worse OS [adjusted hazard ratio (HR adj ) = 2.72, 95% confidence interval (CI) 1.19-6.21, P = 0.02]. Greater increase in TK1 activity after two cycles of chemotherapy resulted in improved event-free survival (HR adj = 0.50, 95% CI 0.26-0.97, P = 0.04) and OS (HR adj = 0.46, 95% CI 0.95, P = 0.04). There was significant interaction between the prognostic value of TK1 kinetics and Ki67 (p interaction 0.04). Conclusion Serial measurement of serum TK1 activity during neoadjuvant chemotherapy provides long-term prognostic information in BC patients. The ease of obtaining serial samples for TK1 assessment motivates further evaluation in larger studies.

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Section: Discussionmentioning

confidence: 99%

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

et al. 2021

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“…A significant proportion of breast cancer cases undergo neoadjuvant therapy before surgery that can even be omitted or at least delayed in the case of multimorbid patients. As neoadjuvant treatment is a main cost driver in breast cancer diagnosis and treatment [6], imaging tests could play a pivotal role in adapting treatment [7]. While therapy could be deescalated in early complete responders, therapy regimes could be changed in non-responders.…”

Section: Therapy Guidance and Monitoringmentioning

confidence: 99%

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Dietzel

Clauser

Kapetas

et al. 2021

Rofo

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Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

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“…This limits the use of MRI as a definitive imaging surrogate for surgical exploration to confirm pCR, although several studies are investigating approaches combining imaging and biopsy to explore the potential to avoid surgery. [12][13][14] Perhaps more relevant to the personalization of treatment is the ability of imaging to predict treatment outcome when measured early in the course of treatment. Early noninvasive indicators of treatment effectiveness could provide a basis for modifying treatment plans, making de-escalation possible for patients showing excellent response and a recommended change in therapy for those with minimal response.…”

Section: Practical Applicationsmentioning

confidence: 99%

The Way of the Future: Personalizing Treatment Plans Through Technology

Liefaard

Lips

Wesseling

et al. 2021

American Society of Clinical Oncology Educational Book

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Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

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Accuracy of Post–Neoadjuvant Chemotherapy Image-Guided Breast Biopsy to Predict Residual Cancer

Cited by 64 publications

References 23 publications

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

The Way of the Future: Personalizing Treatment Plans Through Technology

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