Abstract CT317: Quantitative ultrasound for personalized chemotherapy in locally advanced breast cancer: Clinical trial results
Bibliographic record
Abstract
Abstract Many cancer therapies are intended to induce cell death within a target tumour. A substantial body of research using in vitro and in vivo models has demonstrated that cell death can be detected via quantitative ultrasound techniques. This study investigated the potential to quantify tumour responses to therapy in patients, using quantitative spectral and textural biomarkers extracted from low-frequency ultrasound data (4-10 MHz). Results demonstrate for the first time in a large cohort of patients the ability to predict clinical responders from non-responders as early as one week after the start of chemotherapy with over 95% sensitivity and 95% specificity. A clinical study was undertaken investigating the efficacy of ultrasound to quantify cell death in tumor responses with cancer treatment. Patients (n = 100) with locally advanced breast cancer received anthracyline and taxane-based chemotherapy treatments over four to six months. Data collection consisted of acquiring tumor images and radiofrequency data prior to treatment onset and at 4 times during neoadjuvant chemotherapy (weeks 0, 1, 4, 8 and pre-operatively). Data collection was carried out using an Ultrasonix-RP and an L15-5 6cm transducer pulsed at frequencies of ∼5 and ∼7 MHz, respectively. The majority of patients went on to have a modified radical mastectomy and correlative whole mount histopathology. Results obtained from both ∼5 and ∼7 MHz data indicated considerable increases in ultrasound spectral backscatter power in patients who clinically responded to treatment within one week of starting their chemotherapy. This was accompanied by significant increases in quantitative ultrasound spectral parameters such as mid-band-fit (up to 9.1 ± 1.2 dBr) and 0-MHz intercept (up to 10.8 ± 2.4 dBr). Patients categorized as poor responders clinically demonstrated significantly lower increases (1.9 ± 1.1 dBr and 1.4 ± 2.7 dBr for mid-band-fit and 0-MHz intercept, respectively). Textural biomarkers extracted from quantitative ultrasound spectral parametric maps also demonstrated considerable differences in trend between treatment responding and non-responding patients early after the treatment initiation. Statistically significant differences were found between treatment responding and non-responding patient populations using quantitative ultrasound spectral biomarkers 4 and 8 weeks after treatment initiation. Applying quantitative ultrasound textural biomarkers in order to incorporate response heterogeneities resulted in statistically significant differences between these two populations only one week after the start of chemotherapy. There were also associated survival differences between the two groups of patients. Patients with ultrasound-detected responses at week 1 had an over 90+/-5% 3 year survival whereas patients with no ultrasound-detected response at the same time had a 30+/10% survival (p<0.05). This study demonstrates the potential of ultrasound to quantify changes in tumours in response to cancer treatment administration in a clinical setting. The results indicate that such responses can be detected early during a course of chemotherapy. This can potentially permit ineffective treatments to be changed to more efficacious ones potentially leading to improved treatment outcomes within a framework of personalized medicine. Citation Format: Gregory Jan Czarnota, Ali Sadeghi-Naini, Hadi Tadayyon, Lakshmanan Sannachi, Mehrdad Gangeh, William Tran, Frances Wright, Sonal Gandhi, Kathleen Pritchard, Sunil Verma, Maureen Trudeau. Quantitative ultrasound for personalized chemotherapy in locally advanced breast cancer: Clinical trial results. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr CT317. doi:10.1158/1538-7445.AM2015-CT317
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".