Predicting the response to Neoadjuvant Chemotherapy. Can the addition of tomosynthesis improve the accuracy of CESM? A comparison with breast MRI.
Savaridas, S.; Vinnicombe, S. L.; Warwick, V.; Evans, A.
Show abstract
BackgroundNeoadjuvant chemotherapy (NACT) is used to downstage breast cancer prior to surgery. Image monitoring is essential to guide treatment and to assess in vivo chemosensitivity. Breast MRI is considered the gold-standard imaging technique; however, it is contraindicated or poorly tolerated in some patients and may be hard to access. Evidence suggests contrast enhanced spectral mammography (CESM) may approach the accuracy of MRI. This novel pilot study investigates whether the addition of digital breast tomosynthesis (DBT) to CESM increases the accuracy of response prediction. ResultsSixteen cancers in fourteen patients were imaged with CESM+DBT and MRI following completion of NACT. Ten cancers demonstrated pathological complete response (pCR) defined as absence of residual invasive disease. Greatest accuracy for predicting pCR was with CESM contrast-enhancement only (accuracy 81.3%, sensitivity 100%, specificity 57.1%), followed by MRI (accuracy 62.5%, sensitivity 44.4%, specificity 85.7%). Concordance with invasive tumour size was greater for CESM than MRI, concordance-coefficients 0.70 vs 0.66 respectively. MRI demonstrated greatest concordance with whole tumour size followed by CESM contrast-enhancement plus microcalcification, concordance-coefficients 0.86 vs 0.69. The addition of DBT did not improve accuracy for prediction of pCR or residual disease size. Whereas CESM+DBT tended to underestimate size of residual disease, MRI tended to overestimate but no significant differences were seen (p>0.05). ConclusionsCESM contrast-enhancement plus microcalcification is similar to MRI for predicting residual disease post-NACT. Size of enhancement alone demonstrates best concordance with invasive disease. Inclusion of residual microcalcification improves concordance with DCIS. The addition of DBT to CESM does not improve accuracy. HighlightsO_LINo benefit of adding DBT to CESM for NACT response prediction C_LIO_LICESM appears similar to MRI for predicting response to NACT C_LIO_LICESM has greatest accuracy for residual invasive tumour size. C_LIO_LICESM+calcification has greater accuracy for predicting residual in situ disease. C_LI
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy 93%
- Investigating the relationship between breast cancer risk factors and an AI-generated mammographic texture feature in the Nurses' Health Study II 92%
- Characterization of Body Composition Dynamics Throughout Treatment in Patients with Early-Stage Breast Cancer 91%
Similar papers in this journal
- Improved accuracy of breast volume calculation from 3D surface imaging data using statistical shape models 94%
- Postmastectomy Radiotherapy in pN1 Breast Cancer: Survival Outcomes and Prognostic Factors From a Single-Institution Cohort 93%
- Classification performance bias between training and test sets in a limited mammography dataset 93%
Similar papers in this journal
- Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density 93%
- Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches 91%
- Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality 90%
Similar papers in this journal
- Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature 96%
- Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals 94%
- A prospective study on tumour response assessments methods after neoadjuvant endocrine therapy in early oestrogen receptor positive breast cancer 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.