Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2308.14369

Improving Lesion Volume Measurements on Digital Mammograms

Nikita Moriakov·Jim Peters·Ritse Mann·Nico Karssemeijer·Jos van Dijck·Mireille Broeders·Jonas Teuwen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammograms, which are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95% CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95% CI 0.73 - 0.87] for consistency and 0.78 [95% CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth.

Explore related subjects

physics.med-phcs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nikita Moriakov, Jim Peters, Ritse Mann, Nico Karssemeijer, Jos van Dijck, Mireille Broeders, Jonas Teuwen. 2023-08-28. Improving Lesion Volume Measurements on Digital Mammograms. https://arxiv.org/abs/2308.14369

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy

Head-and-neck anatomy changes over a six-to-seven-week proton course, and the anatomy of a later week cannot be imaged when the plan is made. We present a digital-twin framework that forecasts a patient's treatment-day anatomy as an ensemble of predicted CTs with propagated contours and quantifies the uncertainty of the forecast contours. The twin is a library of previously treated patients with planning and weekly quality-assurance CTs (QACTs), made patient-specific by a two-step foundation-model deformable registration: a cross-patient field carries each library patient onto the current patient, and a longitudinal field, estimated in the current patient's frame, carries that patient's planning-to-QACT change onto the current patient's own planning CT. A library of 302 observations from 88 patients yields about 300 replicates per patient, each a deformation that occurred in a treated patient. The dispersion of the propagated contours, resolved by outward normal, is six-direction contour uncertainty in millimeters. This is uncertainty in the input to the forecast, which library patient the current patient follows, rather than in model parameters, and it is unchanged when the registration engine is exchanged. On ten patients with clinician contours on two QACTs, the library alone fixes the anisotropic shape of the uncertainty (4.5 to 6.2 mm); the first QACT narrows it by a factor of 3.2 to 3.6 without a contour being drawn; an approved contour improves the center but not the width. The estimate orders directions correctly but is not Gaussian-calibrated. A clinical target volume expansion is worked out as one application.

physics.med-ph↗
arXiv2026

On phase aberration estimation using common mid-angle speckle correlations

Phase aberrations, despite degrading ultrasound images, also encode valuable information about the spatial distribution of the speed of sound in tissue. In pulse-echo ultrasound, we can quantify them by exploiting speckle correlations. Among existing strategies, correlations between steered acquisitions that share a common mid-angle have proven particularly effective for inferring the speed of sound. Their phases can be linearly related to the phase aberrations undergone by both the incident and reflected wavefronts. This relationship has so far been demonstrated only through geometric arguments based on point reflectors. Here, we develop a rigorous theoretical formalism that extends this relationship to the speckle regime, completing the previously established linear model and clarifying its underlying assumptions. More importantly, we build on this formalism to analyze correlation-phase fluctuations arising from aberration-induced speckle decorrelation. The analysis reveals that phase variance is governed by the relative loss of coherence, which increases approximately linearly with the square of the correlation phases. Local correlation-phase estimates therefore become increasingly uncertain as their magnitude grows. Experimental measurements in a uniform tissue-mimicking phantom show excellent agreement with the predicted variance. Beyond providing a theoretical basis for advancing speed-of-sound imaging, this formalism establishes the accuracy limit of common-mid-angle correlation phases, offering a benchmark for evaluating more advanced aberration-estimation techniques.

physics.med-ph↗
arXiv2026

Buccal-Lingual Analysis and Inflammation Tracking in Oral Soft Tissues Using Quantitative Ultrasound: A Preclinical Study

Four out of 10 adults aged 30 years or older in the USA are impacted by periodontal diseases which span a spectrum of inflammatory conditions. Currently, a subjective, invasive and a semi-quantitative approach, termed bleeding on probing, is employed in clinics for inflammation assessment. The long-term goal of this study is to fill the current clinical diagnostic gap in dentistry by proposing quantitative ultrasound (QUS)-based biomarkers for inflammation diagnosis and monitoring. Here, as one of the early works in this area, we investigated two QUS parameters for characterizing periodontal inflammation in gingival tissues using a longitudinal preclinical porcine study: attenuation coefficient slope (ACS) and backscatter intensity (BSI). Our study included eight pigs imaged intraorally (24 MHz) at four bi-weekly timepoints from week 0 (healthy) to week 6 (post inflammation inoculation) at their interproximal sites of the third premolars (PM3-Mes) from all quadrants. Moreover, we compared gingival tissues surrounding a tooth at lingual/palatal side versus buccal side at the second molar (M2-Dis) in healthy condition. Our results showed that gingival ACS at all inflammation timepoints were significantly lower than healthy gingival ACS (1.69 \pm 0.53 dB/cm.MHz) using a mixed effect analysis (week 6: 1.08 \pm 0.25 dB/cm.MHz). Longitudinal comparison of BSI did not demonstrate any statistical significance. Gingival ACS and BSI at lingual/palatal versus buccal sides of M2-Dis did not exhibit any statistical significance. These ACSs were linearly correlated with R-squared = 0.89 and a correlation slope of 0.87. For BSI, Bland-Altman analysis demonstrated no difference in mean BSI, although variability was high. These findings highlight the promising potential of ultrasonography paired with QUS to complement current standard of care in dentistry.

physics.med-ph↗