Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study

Background: Although the advent of targeted therapy has substantially improved outcomes for HER2+ breast cancer patients, many will still fail to achieve pathological complete response (pCR) following neoadjuvant chemotherapy (NAC). In order to reduce overtreatment among patients resistant to standard HER2-targeted NAC and identify candidates for alternative therapeutic interventions, there is a need for validated markers of anti-HER2 agent benefit. The computational analysis of pretreatment imaging has shown recent promise in identifying responsive breast cancers. However, previous applications have explored response prediction among cohorts of mixed subtype and therapeutic approach, thus limiting its relevance in informing specific therapeutic strategy.

Methods: This study comprised retrospective contrast-enhanced MRI data from a total of 159 patients who received anti-HER2 therapy at 5 institutions. A deep learning (DL) model was trained and tuned using 100 HER2+ breast cancer patients who received neoadjuvant taxane (T), carboplatin (C), trastuzumab (H), and pertuzumab (P) at Institution A, of which 49 achieved pCR (ypT0/is). A convolutional neural network was designed to analyze pre- and post-contrast MRI images acquired before NAC and compute a patient's probability of achieving pCR. Institution A data was split randomly into a 85 patient training cohort, used to directly train the model, and a 15 patient internal validation cohort, used to monitor and improve training progress. Two external, held-out testing datasets were used to evaluate capability to predict response in HER2+: Testing Cohort 1, consisting of 28 patients (16 pCR, 12 non-pCR) treated with TCHP at Institution B, and Testing Cohort 2, consisting of 29 patients (10 pCR, 19 non-pCR) who received TCH at one of 3 other institutions as part of the BrUOG 211B trial. A multivariable clinical model (MCM) incorporating age, ER/PR status, stage, and size was evaluated separately and in combination with the neural network. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results: The neural network was able to strongly predict response to HER2-targeted NAC in the internal validation (AUC=.93) and testing cohorts (AUC=.84 and AUC=.77). This model offered superior performance compared to a MCM, which performed poorly across institutions. Strikingly, the higher accuracy of DL included correctly identifying responders within the ER+/PR+ subgroup of patients and non-responders within the ER-/PR- subgroup. Combining DL predictions with the clinical model improved performance to AUC of 0.89 in testing cohort 1, but did not improve AUC in cohort 2.

Conclusions: DL analysis of breast DCE-MRI could be used to better identify benefit of HER2-targeted therapeutic approaches prior to administration. As the first exploration of automated response prediction from imaging with respect to a targeted NAC approach, this work uniquely has the potential to help guide therapeutic decision-making. Our approach was effective in predicting response to multiple HER2-targeted NAC regimens, with better performance in the cohort who received TCHP (as in the training cohort). The strong performance of this model across 5 institutions is a promising indicator of its robustness and ability to tailor therapy even within clinically-distinct HER2+ patient subpopulations.

Deep learning (DL) and multivariable clinical model (MCM) pCR prediction by cohortCohortModelAUC (%)Sensitivity (%)Specificity (%)Accuracy (%)Validation (n=15, 53% pCR)DL93888687MCM66637173Testing 1 (n=28, 57% pCR)DL85889289MCM54388350DL+MCM89819286Testing 2 (n=29, 34% pCR)DL77708479MCM53208966DL+MCM76808483

Citation Format: Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi. Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-13.

Abstract P1-10-06: Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Background: Angiogenesis is crucial to a tumor's growth and an important factor in therapeutic outcome. Although quantitative analysis of tumors on dynamic contrast enhanced (DCE) MRI can provide indirect characterization of a tumor's vascularization, direct computational analysis of the tumor-associated vessel network remains a promising, but under-explored potential marker of therapeutic response. For instance, surrounding vasculature with a convoluted 3-dimensional shape and poor blood flow may indicate a more aggressive tumor and poorly facilitate delivery of therapeutic agents. In this work, we present a computational approach for the prediction of neoadjuvant chemotherapy response using quantitative imaging features describing the morphology and function of tumor associated vasculature on pretreatment MRI.

Methods: 243 patients who received DCE-MRI scans prior to neo-adjuvant chemotherapy (NAC) at institution A [n=83], B [n=76], or one of nine other institutions as part of the ISPY1 Trial [n=84] were divided randomly into training (n=123) and testing (n=120) sets. 148 patients were HER2- and received neoadjuvant AC-T, while the 95 HER2+ patients were treated with TCHP (ISPY predates anti-HER2 therapy and HER2+ ISPY patients were excluded). 79 patients achieved pathological complete response [pCR, ypT0/is] following NAC. MRI exams were collected with a 1.5 or 3 Tesla scanner in the axial or sagittal plane. A baseline scan and 2-5 scans after injection of a gadolinium-based contrast agent with a median temporal resolution of 2.5 minutes were acquired. A portion of the tumor was manually delineated, then semi-automatically expanded to 3D. Vasculature was isolated from subtraction images with a specialized filtering approach to detect vessel-shaped objects. Features describing the 3D shape and architecture of the tumor-associated vessel network (e.g. curvature, torsion, and local orientation) and functional semi-quantitative pharmacokinetic (PK) measurements of temporal contrast enhancement changes (e.g. signal enhancement ratio, time to peak enhancement, and rates of uptake and washout) were calculated. Performance was assessed by area under the receiver operating characteristic curve (AUC), as well as the accuracy, sensitivity, and specificity at the operating point corresponding to the Youden Index. The most discriminating features were determined based on frequency of selection by the random forest classifier.

Results: Within the training set, PK parameters of the vessels (AUC=.66) outperformed relative to the PK of tumor (AUC=.63) and the PK of peritumoral regions (AUC=.64); however, a combination of the three yielded best performance (AUC=.75). Vessel shape features alone achieved AUC=.67 in the training set. When multi-region PK features and tumor shape features were combined and applied to the 120-patient independent testing set, the random forest classifier achieved an AUC of 0.70 and identified 81% of patients who would achieve pCR. Non-pCR was best characterized by increased vessel curvature and PK parameters indicating poor perfusion, such as greater time to peak enhancement, slower uptake rate, and quicker washout.

Conclusions: Our findings suggest that properties of the tumor-associated vessel network, such as its shape and enhancement profile, might provide value in identifying patients who will respond to NAC before administration of treatment.

Accuracy (%)Sensitivity (%)Specificity (%)Full testing set (n=120)678160HER2+ (n=44)709548HER2- (n=76)646365HER2-, HR+ (n=51)678364Triple Negative (n=25)605067

Citation Format: Nathaniel Braman, Prateek Prasanna, Kaustav Bera, Mehdi Alilou, Manasa Vulchi, Maryam Etesami, Paulette Turk, Jame Abraham, Donna Plecha, Anant Madabhushi. Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-06.

Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI

593

Background: HER2-targeted neoadjuvant chemotherapy (NAC) possesses heterogeneous outcomes and currently lacks clinically-accepted markers of response. A means of predicting which patients will benefit prior to the treatment could reduce toxicity and the delay to effective intervention. Computational analysis of MRI via a deep neural network has shown promise in identifying NAC responders among mixed receptor subtype and treatment regimen cohorts, but faces challenges due to reproducibility across institutions and has not yet been explored in the context of HER2-targeted therapy. Here we present a deep learning approach for predicting response to HER2-targeted NAC from pre-treatment MRI. Methods: 100 HER2+ breast cancer patients who received NAC with docetaxel, carboplatin, trastuzumab, and pertuzumab at Cleveland Clinic (CCF) and had pre-treatment contrast-enhanced MRI’s were included in this analysis. 49 patients achieved pathological complete response (pCR, ypT0/is), while 51 patients retained presence of residual disease following NAC (non-pCR). 85 patients were used to train a convolutional neural network to predict pCR based on pre- and post-contrast MRI images, and the model design was optimized based on performance within a 15 patient internal validation cohort. An external, held-out testing dataset consisting of 28 patients (16 pCR, 12 non-pCR) imaged and treated at University Hospitals (UH) Cleveland Medical Center was used to validate the performance of the model. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A multivariable model incorporating age, hormone receptor status, stage, and tumor size was developed and similarly evaluated. Results: The neural network was able to predict the response to HER2-targeted NAC in the internal validation cohort (AUC = 0.93) as well as in an independent cohort from a separate institution (AUC = 0.85). This model offered superior performance compared to a multivariate clinical model, which achieved AUC = 0.67 and AUC = 0.52, in internal validation and external held-out testing cohorts, respectively. Conclusions: Deep learning analysis of contrast-enhanced MRI could be used to better target anti-HER2 therapy by pre-treatment prediction of response.[Table: see text]

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

IMPORTANCE

There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer.

OBJECTIVE

To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy.

DESIGN, SETTING, AND PARTICIPANTS

In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019.

MAIN OUTCOMES AND MEASURES

Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting.

RESULTS

In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002).

CONCLUSIONS AND RELEVANCE

A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.

Abstract P1-06-09: Correlation of breast cancer index (BCI) results to lymphovascular invasion in early stage HR+ breast cancer

Background: Positive lymphovascular invasion (LVI) is a negative prognostic factor for women with early-stage ER+ breast cancer. LVI, along with other clinicopathologic factors such as larger tumor size, higher grade and positive nodal status, increase a patient's risk of late (post-5y) recurrence. Breast Cancer Index (BCI) is a validated gene expression-based assay for patients with early-stage HR+ breast cancer that reports an individualized risk of late distant recurrence based on a combination of the HOXB13/I17BR ratio and the molecular grade index (MGI). The correlation of LVI and individualized risk stratification by genomic analysis is not well characterized. Therefore, this study evaluated risk stratification by BCI based on the presence or absence of LVI.

Methods: A population of 2,613 patients with known LVI status were identified in the Breast Cancer Index Clinical Database for Correlative Studies, an IRB-approved de-identified database which contains clinicopathologic and molecular variables of more than 19,000 clinical cases submitted for BCI testing. LVI was recorded as either positive or negative based on pathology report review. BCI results based on LVI status from LN- (n=2035) and LN+ patients (n=578) were evaluated separately. Chi-squared tests were used to compare BCI results between LVI groups.

Results: In analyses of 2,613 patients with LVI data available (median age 59.1 y; range 28-89y; 74% ≥50y), 18.3% of patients showed evidence of LVI (LVI-pos). In comparison to the LVI-neg tumors submitted for BCI testing, the LVI-pos tumors had a higher proportion of grade 3 tumors (33% vs 16%, p&lt;0.0001), more LVI-pos tumors were 2.0 cm or greater (45% vs 23%, p&lt;0.0001), a higher percentage LVI-pos patients had node-positive disease (51% vs 16%, p&lt;0.0001), and a higher proportion of LVI-pos tumors showed high Ki67 (Ki67 ≥14%; 64% vs 51%, p=0.004). A correlation between LVI positivity and high BCI prognostic risk was observed, with a higher proportion of LVI-pos patients classified as high risk of late distant recurrence in both the LN- (68% vs 49%, p&lt;0.0001) and LN+ subsets (84% vs 70%, p&lt;0.0001) compared to LVI-neg patients. LVI-pos patients had a higher median molecular proliferative status (MGI) compared to LVI-neg patients regardless of nodal status (p&lt;0.0001 for both). In contrast to the categorical LVI prognostic factor, the wide distribution of BCI individual risk scores provides additional resolution that identifies a substantial subset of LVI positive tumors (32%) that that have a low risk of late recurrence by genomic analysis.

Conclusion: While BCI Prognostic stratification correlated with LVI status, BCI identified a subset of patients with LVI positivity as having a low risk of late distant recurrence that otherwise would have an unfavorable prognosis based on LVI and/or LN positivity. These findings help to characterize differential patient stratification based on an individualized assessment of tumor biology versus LVI for patients considering EET.

Citation Format: Vulchi M, Sagalnik M, Schnabel CA, Abraham J. Correlation of breast cancer index (BCI) results to lymphovascular invasion in early stage HR+ breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-06-09.

The changing anatomic position of squamous cell carcinoma of the lung - a new conundrum

BACKGROUND

Traditionally, squamous cell carcinoma (SCC) of the lung is a central rather than a peripheral form of lung cancer. Rates of SCC in the lung periphery are typically sited in the 15-30% range. Recently, we observed that a significant portion of newly diagnosed SCC was located on a periphery. A comprehensive review of the tumor data at our facility, a busy teaching hospital with a large cohort of cancer patients, was undertaken to assess whether there had been a substantive change in the traditional epidemiologic distributions of the lung cancer, specifically with respect to SCC. Given the differences in cell biology and carcinogenesis of central versus peripheral SCC, a potential epidemiologic shift might suggest a change in tumor biology.

METHODS

From May 12, 2012 through May 13, 2013, all histopathologically confirmed diagnoses of SCC of the lung were retrospectively reviewed. Each patient's lesion was then classified as peripheral or central based on CT evidence.

RESULTS

A total of 56 patients were diagnosed with SCC. Of these, 55% (n=31) had peripheral and 45% (n=25) had central SCC. Twenty-nine patients did not have any prior history of malignancy. Of this subset of patients, 62% (n=18) had peripheral SCC, and 38% (n=11) had central SCC.

CONCLUSION

Our findings appear to correlate with our initial observation that, within our institution, there has been a substantive shift in the traditional distribution of SCC with the majority of these cancers now being diagnosed in the lung periphery as opposed to the more central locations.