Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Phase 2 study of neoadjuvant enzalutamide and paclitaxel for luminal androgen receptor-enriched TNBC: Trial results and insights into "ARness".

Luminal androgen receptor (LAR)-enriched triple-negative breast cancer (TNBC) is a distinct subtype. The efficacy of AR inhibitors and the relevant biomarkers in neoadjuvant therapy (NAT) are yet to be determined. We tested the combination of the AR inhibitor enzalutamide (120 mg daily by mouth) and paclitaxel (80 mg/m2 weekly intravenously) (ZT) for 12 weeks as NAT for LAR-enriched TNBC. Eligibility criteria included a percentage of cells expressing nuclear AR by immunohistochemistry (iAR) of at least 10% and a reduction in sonographic volume of less than 70% after four cycles of doxorubicin and cyclophosphamide. Twenty-four patients were enrolled. Ten achieved a pathologic complete response or residual cancer burden-I. ZT was safe, with no unexpected side effects. An iAR of at least 70% had a positive predictive value of 0.92 and a negative predictive value of 0.97 in predicting LAR-enriched TNBC according to RNA-based assays. Our data support future trials of AR blockade in early-stage LAR-enriched TNBC.

ACR Appropriateness Criteria® Pretreatment Evaluation and Follow-Up of Invasive Cancer of the Cervix: 2023 Update.

Cervical cancer is a common gynecological malignancy worldwide. Cervical cancer is staged based on the International Federation of Gynecology and Obstetrics (FIGO) classification system, which was revised in 2018 to incorporate radiologic and pathologic data. Imaging plays an important role in pretreatment assessment including initial staging and treatment response assessment of cervical cancer. Accurate determination of tumor size, local extension, and nodal and distant metastases is important for treatment selection and for prognostication. Although local recurrence can be diagnosed by physical examination, imaging plays a critical role in detection and follow-up of local and distant recurrence and subsequent treatment selection. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Pretreatment evaluation and follow-up of invasive cancer of the cervix: 2023 update

Cervical cancer is a common gynecological malignancy worldwide. Cervical cancer is staged based on the International Federation of Gynecology and Obstetrics (FIGO) classification system, which was revised in 2018 to incorporate radiologic and pathologic data. Imaging plays an important role in pretreatment assessment including initial staging and treatment response assessment of cervical cancer. Accurate determination of tumor size, local extension, and nodal and distant metastases is important for treatment selection and for prognostication. Although local recurrence can be diagnosed by physical examination, imaging plays a critical role in detection and follow-up of local and distant recurrence and subsequent treatment selection. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Designing clinical trials for patients who are not average.

The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.

Patient Interest in Exploring Nonsurgical Treatment Approaches for Early-Stage Breast Cancer: A Qualitative Study.

PURPOSE: Advances in radiation therapy have enabled the ability to deliver ablative treatments, but there has been limited application of these treatments to early-stage breast cancers with a goal of omitting surgery. The purpose of this study was to explore patient interest in pursuing nonsurgical treatment approaches for their early-stage breast cancer. METHODS AND MATERIALS: We conducted a qualitative study involving interviews with 21 patients with early-stage breast cancer who were eligible for participation in a phase 2 clinical trial offering omission of definitive surgery. Interviews were transcribed and an inductive, thematic analysis was performed by 3 independent reviewers to generate themes and subthemes. RESULTS: Data analysis revealed the following factors that affected patient willingness and desire to explore nonsurgical treatment options: (1) perceptions and feelings about their cancer; (2) current quality of life and the level of support available in their daily life; (3) external conversations focusing on family members' and friends' experiences with cancer and/or cancer treatments; (4) personal health care experiences, including their current breast cancer diagnosis; (5) perceptions and feelings about their physicians; (6) conversations with their physicians about their treatment options; and (7) self-identified desire to direct care decisions. Specifically, patients verbalized fearing surgery and surgical recovery; wanting to preserve their breast(s); the prior negative surgical experiences of friends, family, and themselves; a desire to receive treatment per the latest research; wanting to match the level of treatment with the severity of their cancer; and other comorbidities as reasons for wanting to explore omitting surgery. CONCLUSIONS: Our findings demonstrate an unmet need directed by patient interest to explore nonsurgical options for early-stage, biologically favorable breast cancer. These results may shape conversations around shared decision-making and clinical trial design, and result in more personalized treatment options for women with early-stage breast cancer.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment.

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Patient-Reported Outcomes of Omission of Breast Surgery Following Neoadjuvant Systemic Therapy: A Nonrandomized Clinical Trial.

Importance: Patients should have an active role in decisions about pursuing or forgoing specific therapies in treatment de-escalation trials. Objective: To evaluate longitudinal patient-reported outcomes (PROs) encompassing decisional comfort and health-related quality of life (HRQOL) among patients who elected to enroll in a clinical trial evaluating radiotherapy alone, without breast surgery, for invasive breast cancers with exceptional response to neoadjuvant systemic therapy (NST). Design, Setting, and Participants: Prospective, single-group, phase 2 clinical trial at 7 US medical centers. Women aged 40 years or older with invasive cT1-2 N0-1 M0 triple-negative or human epidermal growth factor receptor 2 (ERBB2)-positive breast cancer with no pathologic evidence of residual disease following standard NST enrolled from March 6, 2017, to November 9, 2021. Validated PRO measures were administered at baseline and 6, 12, and 36 months post-radiotherapy. Data were analyzed from January to February 2023. Interventions: PRO measures included the Decision Regret Scale (DRS), Functional Assessment of Cancer Therapy-Lymphedema (FACT-B+4), and Breast Cancer Treatment Outcomes Scale (BCTOS). Main Outcomes and Measures: Changes in PRO measure scores and subscores over time. Results: Among 31 patients, the median (IQR) age was 61 (56-66) years, 26 (84%) were White, and 26 (84%) were non-Hispanic. A total of 15 (48%) had triple-negative disease and 16 (52%) had ERBB2-positive disease. Decisional comfort was high at baseline (median [IQR] DRS score 10 [0-25] on a 0-100 scale, with higher scores indicating higher decisional regret) and significantly increased over time (median [IQR] DRS score at 36 months, 0 [0-20]; P < .001). HRQOL was relatively high at baseline (median [IQR] FACT-B composite score 121 [111-134] on a 0-148 scale, with higher scores indicating higher HRQOL) and significantly increased over time (median [IQR] FACT-B score at 36 months, 128 [116-137]; P = .04). Perceived differences between the affected breast and contralateral breast were minimal at baseline (median [IQR] BCTOS score 1.05 [1.00-1.23] on a 1-4 scale, with higher scores indicating greater differences) and increased significantly over time (median [IQR] BCTOS score at 36 months, 1.36 [1.18-1.64]; P < .001). At 36 months postradiotherapy, the cosmetic subscore was 0.45 points higher than baseline (95% CI, 0.16-0.74; P = .001), whereas function, pain, and edema subscores were not significantly different than baseline. Conclusions and Relevance: In this nonrandomized phase 2 clinical trial, analysis of PROs demonstrated an overall positive experience for trial participants, with longitudinal improvements in decisional comfort and overall HRQOL over time and minimal lasting adverse effects of therapy. Trial Registration: ClinicalTrials.gov Identifier: NCT02945579.

Feasibility of quantitative diffusion-weighted imaging during intra-procedural MRI-guided brachytherapy of locally advanced cervical and vaginal cancers.

PURPOSE: To determine the feasibility of quantitative apparent diffusion coefficient (ADC) acquisition during magnetic resonance imaging-guided brachytherapy (MRgBT) using reduced field-of-view (rFOV) diffusion-weighted imaging (DWI). METHODS AND MATERIALS: T2-weighted (T2w) MR and full-FOV single-shot echo planar (ssEPI) DWI were acquired in 7 patients with cervical or vaginal malignancy at baseline and prior to brachytherapy, while rFOV-DWI was acquired during MRgBT following brachytherapy applicator placement. The gross target volume (GTV) was contoured on the T2w images and registered to the ADC map. Voxels at the GTV's maximum Maurer distance comprised a central sub-volume (GTVcenter). Contour ADC mean and standard deviation were compared between timepoints using repeated measures ANOVA. RESULTS: ssEPI-DWI mean ADC increased between baseline and prebrachytherapy from 1.03 ± 0.18 10-3 mm2/s to 1.34 ± 0.28 10-3 mm2/s for the GTV (p = 0.06) and from 0.84 ± 0.13 10-3 mm2/s to 1.26 ± 0.25 10-3 mm2/s at the level of the GTVcenter (p = 0.03), consistent with early treatment response. rFOV-DWI during MRgBT demonstrated mean ADC values of 1.28 ± 0.14 10-3 mm2/s and 1.28 ± 0.19 10-3 mm2/s for the GTV and GTVcenter, respectively (p = 0.02 and p = 0.03 relative to baseline). No significant differences were observed between ssEPI-DWI and rFOV-DWI ADC measurements. CONCLUSIONS: Quantitative ADC measurement in the setting of MRI guided brachytherapy implant placement for cervical and vaginal cancers is feasible using rFOV-DWI, with comparable mean ADC comparable to prebrachytherapy ssEPI-DWI, and may enable MRI-guided radiotherapy targeting of low ADC, radiation resistant sub-volumes of tumor.

PTEN in triple-negative breast carcinoma: protein expression and genomic alteration in pretreatment and posttreatment specimens.

Background: Recent advances have been made in targeting the phosphoinositide 3-kinase pathway in breast cancer. Phosphatase and tensin homolog (PTEN) is a key component of that pathway. Objective: To understand the changes in PTEN expression over the course of the disease in patients with triple-negative breast cancer (TNBC) and whether PTEN copy number variation (CNV) by next-generation sequencing (NGS) can serve as an alternative to immunohistochemistry (IHC) to identify PTEN loss. Methods: We compared PTEN expression by IHC between pretreatment tumors and residual tumors in the breast and lymph nodes after neoadjuvant chemotherapy in 96 patients enrolled in a TNBC clinical trial. A correlative analysis between PTEN protein expression and PTEN CNV by NGS was also performed. Results: With a stringent cutoff for PTEN IHC scoring, PTEN expression was discordant between pretreatment and posttreatment primary tumors in 5% of patients (n = 96) and between posttreatment primary tumors and lymph node metastases in 9% (n = 33). A less stringent cutoff yielded similar discordance rates. Intratumoral heterogeneity for PTEN loss was observed in 7% of the patients. Among pretreatment tumors, PTEN copy numbers by whole exome sequencing (n = 72) were significantly higher in the PTEN-positive tumors by IHC compared with the IHC PTEN-loss tumors (p < 0.0001). However, PTEN-positive and PTEN-loss tumors by IHC overlapped in copy numbers: 14 of 60 PTEN-positive samples showed decreased copy numbers in the range of those of the PTEN-loss tumors. Conclusion: Testing various specimens by IHC may generate different PTEN results in a small proportion of patients with TNBC; therefore, the decision of testing one versus multiple specimens in a clinical trial should be defined in the patient inclusion criteria. Although a distinct cutoff by which CNV differentiated PTEN-positive tumors from those with PTEN loss was not identified, higher copy number of PTEN may confer positive PTEN, whereas lower copy number of PTEN would necessitate additional testing by IHC to assess PTEN loss. Trial registration: NCT02276443.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Predictive Roles of Baseline Stromal Tumor-Infiltrating Lymphocytes and Ki-67 in Pathologic Complete Response in an Early-Stage Triple-Negative Breast Cancer Prospective Trial.

High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associated with pCR could enhance our ability to predict the group of patients on whom treatment de-escalation strategies could be tested. In this prospective early-stage TNBC neoadjuvant chemotherapy study, pretreatment biopsies from 408 patients were evaluated for their clinical and demographic features, as well as biomarkers including sTILs, Ki-67, PD-L1 and androgen receptor. Multivariate logistic regression models were developed to generate a computed response score to predict pCR. The pCR rate for the entire cohort was 41%. Recursive partitioning analysis identified ≥20% as the optimal cutoff for sTILs to denote 35% (143/408) of patients as having high sTILs, with a pCR rate of 59%, and 65% (265/408) of patients as having low sTILs, with a pCR rate of 31%. High Ki-67 (cutoff > 35%) was identified as the only predictor of pCR in addition to sTILs in the training set. This finding was verified in the testing set, where the highest computed response score encompassing both high sTILa and high Ki-67 predicted a pCR rate of 65%. Integrating Ki67 and sTIL may refine the selection of early stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies.

Key clinical trials in rectal cancer shaping the current treatment paradigms: reference guide for radiologists.

Total neoadjuvant therapy (TNT), which includes chemotherapy and radiation prior to surgical resection, has been recently accepted as the new standard of care for patients with locally advanced low and mid rectal cancers. Multiple clinical trials have evaluated this approach in the last several decades and demonstrated improvement in, local control and reduced risk of recurrence. In addition, in the course of these investigations, it has been shown that between a third and a half of patients experience a clinical complete response (cCR) after being treated with the TNT approach, leading to the development of new organ preservation protocol, now known as watch-and-wait (W&W). On this protocol, cCR patients are not referred for surgery after total neoadjuvant treatment. Instead, they remain on close surveillance and, thus, avoid potential complications associated with surgical resection. Multiple clinical trials are ongoing, investigating the long-term outcomes of these new approaches and the development of less toxic and more effective TNT regimens for LARC. Improvements in technology and rectal MRI protocols position radiologists as vital members of multidisciplinary rectal cancer management teams. Rectal MRI has become a critical tool for rectal cancer initial staging, treatment response assessment, and surveillance on W&W protocols. In this review, we summarize the findings of the pivotal clinical trials that contributed to establishing the current treatment paradigms in locally advanced rectal cancer (LARC) management, with the intention of helping radiologists play more effective roles in their multidisciplinary teams.

Molecular Breast Imaging and Positron Emission Mammography.

There is growing interest in application of functional imaging modalities for adjunct breast imaging due to their unique ability to evaluate molecular/pathophysiologic changes, not visible by standard anatomic breast imaging. This has led to increased use of nuclear medicine dedicated breast-specific single photon and coincidence imaging systems for multiple indications, such as supplemental screening, staging of newly diagnosed breast cancer, evaluation of response to neoadjuvant treatment, diagnosis of local disease recurrence in the breast, and problem solving. Studies show that these systems maybe especially useful for specific subsets of patients, not well served by available anatomic breast imaging modalities.

Follow-up imaging of anal cancer after treatment.

Anal cancer treatment response assessment can be challenging with both magnetic resonance imaging (MRI) and clinical evaluation considered essential. MRI, in particular, has shown to be useful for the assessment of treatment response, the detection of recurrent disease in follow up and surveillance, and the evaluation of possible post-treatment complications as well as complications from the tumor itself. In this review, we focus on the role of imaging, mainly MRI, in anal cancer treatment response assessment. We also describe the treatment complications that can occur, and the imaging findings associated with those complications.