Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials

Role of imaging based response assesment for adapting neoadjuvant systemic therapy for breast cancer: A systematic review

PURPOSE

The objective of this systematic review is to investigate the role of imaging in response monitoring during neoadjuvant systemic therapy (NST) for breast cancer and assess whether treatment modifications based on imaging response are implemented in clinical practice.

METHODS

A systematic review was conducted, analyzing five clinical practice guidelines and 147 clinical trial publications involving NST for breast cancer. The snowballing technique was employed, using a "start set" of clinical guidelines to trace relevant trials. Additionally, a PubMed search was conducted to identify trials published between 2023-2024. The review analyzed the use of imaging modalities, timing, and response criteria, and whether escalation, de-escalation, or change of treatment occurred based on imaging response.

RESULTS

Imaging was utilized in 81 % (119/147) of the trials, with ultrasound, MRI, and mammography being the most frequently employed modalities. Mid-treatment imaging was applied in 56 % (83/147) of the trials. However, only 15 % (22/147) of the trials implemented treatment modifications based on imaging response, highlighting the limited application of imaging response-guided therapy. No standardized imaging protocols or consistent response-guided treatment strategies were identified across the trials or clinical practice guidelines, with considerable variability in imaging methods, timing, and response criteria.

CONCLUSION

This systematic review underscores the critical need for standardized imaging protocols, response assessment criteria and image-guided treatment decisions. It is therefore evident that imaging for response monitoring during treatment should preferably be performed within clinical trials.

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Comparison of automated breast volume scanning with conventional ultrasonography, mammography, and MRI to assess residual breast cancer after neoadjuvant therapy by molecular type

AIM

To compare the accuracy of hand-held ultrasonography (US), mammography (MG), magnetic resonance imaging (MRI), and automated breast volume scanning (ABVS) in defining residual breast cancer tumour size after neoadjuvant therapy (NAT).

MATERIALS AND METHODS

Patients diagnosed breast cancer and who received NAT at the Breast Center, Peking University People's Hospital, were enrolled prospectively. Imaging was performed after the last cycle of NAT. The residual tumour size, intraclass correlation coefficients (ICCs), and receiver operating characteristic (ROC) to predict pathological complete response (pCR) were analysed.

RESULTS

A total of 156 patients with 159 tumours were analysed. ABVS had a moderate correlation with histopathology residual tumour size (ICC = 0.666), and showed high agreement among triple-positive tumours (ICC = 0.797). With 5 mm as the threshold, the coincidence rate reached 64.7% between ABVS and pathological size, which was significantly higher than that between US, MG, MRI, and pathological size (50%, 45.1%, 41.4%; p=0.009, p=0.001, p<0.001, respectively). For ROC analysis, ABVS demonstrated a higher area under the ROC curve, but with no statistical difference, except for MG (0.855, 0.816, 0.819, and 0.788, respectively; p=0.183 for US, p=0.044 for MG, and p=0.397 for MRI, with ABVS as the reference).

CONCLUSIONS

The longest tumour diameter on ABVS had a moderate correlation with pathological residual invasive tumour size. ABVS was shown to have good ability to predict pCR and would appear to be a potential useful tool for the assessment after NAT for breast cancer.