Multiparametric MRI as a Predictor of PSA Response in Patients Undergoing Stereotactic Body Radiation Therapy for Prostate Cancer

Purpose

To maximize the therapeutic ratio, it is important to identify adverse prognostic features in men with prostate cancer, especially among those with intermediate risk disease, which represents a heterogeneous group. These men may benefit from treatment intensification. Prior studies have shown pretreatment mpMRI may predict biochemical failure in patients with intermediate and/or high-risk prostate cancer undergoing conventionally fractionated external beam radiation therapy and/or brachytherapy. This study aims to evaluate pretreatment mpMRI findings as a marker for outcome in patients undergoing stereotactic body radiation therapy (SBRT).

Methods and Materials

We identified all patients treated at our institution with linear accelerator based SBRT to 3625 cGy in 5 fractions, with or without androgen deprivation therapy (ADT) from November 2015 to March 2021. All patients underwent pretreatment Magnetic Resonance Imaging (MRI). Posttreatment Prostate Specific Imaging (PSA) measurements were typically obtained 4 months after SBRT, followed by every 3 to 6 months thereafter. A 2 sample t test was used to compare preoperative mpMRI features with clinical outcomes.

Results

One hundred twenty-three men were included in the study. Pretreatment MRI variables including median diameter of the largest intraprostatic lesion, median number of prostate lesions, and median maximal PI-RADS score, were each predictive of PSA nadir and time to PSA nadir (P < .0001). When separated by ADT treatment, this association remained for patients who were not treated with ADT (P < .001). In patients who received ADT, the pretreatment MRI variables were each significantly associated with time to PSA nadir (P < .01) but not with PSA nadir (P > 0.30). With a median follow-up time of 15.9 months (IQR: 8.5-23.3), only 3 patients (2.4%) experienced biochemical recurrence as defined by the Phoenix criteria.

Conclusions

Our experience shows the significant ability of mpMRI for predicting PSA outcome in prostate cancer patients treated with SBRT with or without ADT. Since PSA nadir has been shown to correlate with biochemical failure, this information may help radiation oncologists better counsel their patients regarding outcome after SBRT and can help inform future studies regarding who may benefit from treatment intensification with, for example, ADT and/or boosts to dominant intraprostatic lesions.

Scorecards: Quantifying Dosimetric Plan Quality in Pancreatic Ductal Adenocarcinoma Stereotactic Body Radiation Therapy

Purpose

A scoring mechanism called the scorecard that objectively quantifies the dosimetric plan quality of pancreas stereotactic body radiation therapy treatment plans is introduced.

Methods and Materials

A retrospective analysis of patients with pancreatic ductal adenocarcinoma receiving stereotactic body radiation therapy at our institution between November 2019 and November 2020 was performed. Ten patients were identified. All patients were treated to 36 Gy in 5 fractions, and organs at risk (OARs) were constrained based on Alliance A021501. The scorecard awarded points for OAR doses lower than those cited in Alliance A021501. A team of 3 treatment planners and 2 radiation oncologists, including a physician resident without plan optimization experience, discussed the relative importance of the goals of the treatment plan and added additional metrics for OARs and plan quality indexes to create a more rigorous scoring mechanism. The scorecard for this study consisted of 42 metrics, each with a unique piecewise linear scoring function which is summed to calculate the total score (maximum possible score of 365). The scorecard-guided plan, the planning and optimization for which were done exclusively by the physician resident with no prior plan optimization experience, was compared with the clinical plan, the planning and optimization for which were done by expert dosimetrists, using the Sign test.

Results

Scorecard-guided plans had, on average, higher total scores than those clinically delivered for each patient, averaging 280.1 for plans clinically delivered and 311.7 for plans made using the scorecard (P = .003). Additionally, for most metrics, the average score of each metric across all 10 patients was higher for scorecard-guided plans than for clinically delivered plans. The scorecard guided the planner toward higher coverage, conformality, and OAR sparing.

Conclusions

A scorecard tool can help clarify the goals of a treatment plan and provide an objective method for comparing the results of different plans. Our study suggests that a completely novice treatment planner can use a scorecard to create treatment plans with enhanced coverage, conformality, and improved OAR sparing, which may have significant effects on both tumor control and toxicity. These tools, including the scorecard used in this study, have been made freely available.

CT-Guided Online Adaptive Stereotactic Body Radiotherapy for Pancreas Ductal Adenocarcinoma: Dosimetric and Initial Clinical Experience

PURPOSE/OBJECTIVE(S)

Retrospective analysis suggests that dose escalation to a biologically effective dose of more than 70 Gy may improve overall survival in patients with pancreatic ductal adenocarcinoma (PDAC), but such treatments in practice are limited by proximity of organs at risk (OARs). We hypothesized that CT-guided online adaptive radiotherapy (OART) can account for interfraction movement of OARs, reduce dose to OARs, and improve coverage of targets.

MATERIALS/METHODS

This is a single institution retrospective analysis of patients with PDAC treated with OART on a CBCT-based OART platform. All patients were treated to 40 Gy in 5 fractions. PTV overlapping with a 5 mm planning risk volume expansion on the stomach, duodenum and bowel received 25 Gy. Initial treatment plans were created conventionally. For each fraction, PTV and OAR volumes were recontoured with AI assistance after initial cone beam CT (CBCT). The adapted plan was calculated, underwent QA, and then compared to the scheduled plan. A second CBCT was obtained prior to delivery of the selected plan. Total treatment time (first CBCT to end of radiation delivery) and active physician time (first to second CBCT) were recorded. PTV_4000 V95%, PTV_2500 V95%, and D0.03 cc to stomach, duodenum and bowel were reported for scheduled (S) and adapted (A) plans. CTCAEv5.0 toxicities were recorded. Statistical analysis was performed using a two-sided T test and α of 0.05.

RESULTS

Seven patients with unresectable or locally-recurrent PDAC were analyzed, with a total of 35 fractions. Average total time was 33:00 minutes (22:25-49:40) and average active time was 22:48 minutes (14:15-39:34). All fractions were treated with adapted plans. All adapted plans met PTV_4000 V95.0% > 95.0% coverage goal and OAR dose constraints. Dosimetric data for scheduled and adapted plans per fraction are in Table 1. Median follow up was 1.7 months. 5 (71%) patients experienced either Grade 1 or 2 toxicities. No patients experienced Grade 3+ toxicities.

CONCLUSION

Daily OART significantly reduced dose OARs while achieving superior PTV coverage. Treatment was generally well tolerated with no grade 3+ acute toxicity, and required only 22:48 minutes on average of active physician time. Our initial clinical experience demonstrates OART allows for safe dose escalation in the treatment of PDAC.

Adaptive Radiation Therapy: A Review of CT-based Techniques

Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. Keywords: Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Impact of Adapted Radiotherapy Schedules on Bowel Sparing in Node-Positive Cervical Cancer

PURPOSE

Definitive radiation therapy (RT) for locally advanced node-positive cervical cancer confers significant toxicity to pelvic organs including the small bowel. Gross nodal disease exhibits significant shrinkage during RT, and yet conventional RT does not account for this change. We evaluated the reduction in absorbed bowel dose using various adaptive RT schedules.

METHODS AND MATERIALS

We obtained 130 evaluable scans (computed tomography simulation and 25 cone beam computed tomography scans per patient) of 5 patients who had received definitive external beam RT for lymph node positive cervical cancer daily over 5 weeks. Using a single universal volumetric modulated arc therapy plan with predefined optimization priorities, we created adapted RT plans in 4 schedules: Daily, Weekly, Twice, and NoAdapt (mimicking conventional nonadapted RT). The in silico (computer modeled) patients were treated to 45 Gy to primary cervical disease with a simultaneous integrated boost to 55 Gy to involved lymph nodes. We evaluated dose metrics including D2cc, D15cc, and V45 to determine the impact of adapted RT schedules on bowel sparing. Statistical tests included the Student t test, analysis of variance, and the Spearman rank correlation.

RESULTS

The quantity of reduced bowel dose was significantly associated with the chosen planning schedule in all evaluated metrics and was proportional to the frequency of adaptive RT with significant moderate-to-strong monotonicity. Both D2cc and D15cc were reduced an average of 2.7 Gy using daily replanning compared with a nonadapted approach. A minimally adapted strategy of only 2 replans also confers a significant dosimetric benefit over a nonadapted approach. Reduced standard deviations of D2cc and V45 bowel doses over the treatment courses were significantly associated with the choice of planning schedule with strong monotonicity.

CONCLUSIONS

All adaptive RT schedules evaluated confer significant dosimetric advantages in bowel sparing over a conventional nonadapted technique, with greater sparing seen with more frequent replanning schedules. These findings warrant future trials of adaptive RT for pelvic malignancies.

Automated detection and segmentation of non-small cell lung cancer computed tomography images

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Preclinical modeling of leiomyosarcoma identifies susceptibility to transcriptional CDK inhibitors through antagonism of E2F-driven oncogenic gene expression

PURPOSE

Leiomyosarcoma (LMS) is a neoplasm characterized by smooth muscle differentiation, complex copy-number alterations, tumor suppressor loss and the absence of recurrent driver mutations. Clinical management for advanced disease relies on the use of empiric cytotoxic chemotherapy with limited activity, and novel targeted therapies supported by preclinical research on LMS biology are urgently needed. A lack of fidelity of established LMS cell lines to their mesenchymal neoplasm of origin has limited translational understanding of this disease, and few other preclinical models have been established. Here, we characterize LMS patient derived xenograft (PDX) models of LMS, assessing fidelity to their tumors of origin and performing preclinical evaluation of candidate therapies.

EXPERIMENTAL DESIGN

We implanted 49 LMS surgical samples into immunocompromised mice. Engrafting tumors were characterized by histology, targeted next-generation sequencing, RNA-seq and ultra-low passage whole-genome sequencing. Candidate therapies were selected based on prior evidence of pathway activation or high-throughput dynamic BH3 profiling.

RESULTS

We show that LMS PDX maintain the histologic appearance, copy-number alterations and transcriptional program of their parental tumors across multiple xenograft passages. Transcriptionally, LMS PDX co-cluster with paired LMS patient-derived samples and differ primarily in host-related immunologic and microenvironment signatures. We identify susceptibility of LMS PDX to transcriptional CDK inhibition, which disrupts an E2F-driven oncogenic transcriptional program and inhibits tumor growth.

CONCLUSIONS

Our results establish LMS PDX as valuable preclinical models and identify strategies to discover novel vulnerabilities in this disease. These data support the clinical assessment of transcriptional CDK inhibitors as a therapeutic strategy for LMS patients.

Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis

Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69–0.90, 90% CI) in NAWM and 0.81 (0.71–0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47–1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10–0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.

Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients

PURPOSE

The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status.

METHOD

209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed.

RESULTS

Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75-0.86) and 0.76 (0.71-0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6-0.82) using cubic interpolation and 0.72 (0.6-0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation).

CONCLUSIONS

MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.

High-throughput dynamic BH3 profiling may quickly and accurately predict effective therapies in solid tumors

Despite decades of effort, the sensitivity of patient tumors to individual drugs is often not predictable on the basis of molecular markers alone. Therefore, unbiased, high-throughput approaches to match patient tumors to effective drugs, without requiring a priori molecular hypotheses, are critically needed. Here, we improved upon a method that we previously reported and developed called high-throughput dynamic BH3 profiling (HT-DBP). HT-DBP is a microscopy-based, single-cell resolution assay that enables chemical screens of hundreds to thousands of candidate drugs on freshly isolated tumor cells. The method identifies chemical inducers of mitochondrial apoptotic signaling, a mechanism of cell death. HT-DBP requires only 24 hours of ex vivo culture, which enables a more immediate study of fresh primary tumor cells and minimizes adaptive changes that occur with prolonged ex vivo culture. Effective compounds identified by HT-DBP induced tumor regression in genetically engineered and patient-derived xenograft (PDX) models of breast cancer. We additionally found that chemical vulnerabilities changed as cancer cells expanded ex vivo. Furthermore, using PDX models of colon cancer and resected tumors from colon cancer patients, our data demonstrated that HT-DBP could be used to generate personalized pharmacotypes. Thus, HT-DBP appears to be an ex vivo functional method with sufficient scale to simultaneously function as a companion diagnostic, therapeutic personalization, and discovery tool.

Abstract 4478: High-throughput dynamic BH3 profiling identifies active cancer therapies in solid tumors

Phenotypic differences between primary patient tumors and models of tumors that are amenable to chemical screening represents a key barrier to drug discovery and drug repurposing. High-throughput technologies that can accurately identify active drugs using primary patient tumors are therefore valuable additions to the drug development toolbox and may have direct clinical utility as predictive biomarkers. Here we report a method called high-throughput dynamic BH3 profiling (HT-DBP), a robust microscopy-based assay with single-cell resolution that enables chemical screens of hundreds of candidate drugs on freshy isolated tumor cells to identify chemical inducers of mitochondrial apoptotic signaling. HT-DBP requires only 24 hours of ex vivo culture which enables the direct study of fresh primary tumor cells and minimizes adaptive changes that occur with prolonged ex vivo culture.

Using 1650 compounds, we performed HT-DBP on freshly isolated cells from the MMTV-PyMT genetically engineered mouse model of breast cancer to identify drugs that sensitize these tumors for apoptosis. Selected compounds identified by HT-DBP induced regressions in the MMTV-PyMT genetically engineered mouse model of breast cancer in vivo, and in a patient derived xenograft model of breast cancer. To evaluate how chemical vulnerabilities evolve in cell culture, we performed HT-DBP on a cancer cell line from MMTV-PyMT tumors. We observe different patterns of chemical vulnerabilities between freshly isolated tumor cells and in the derived cell lines, which is consistent with the mixed track record of cancer cell line chemical screens.

We demonstrate that HT-DBP can be used to generate personalized apoptotic chemical vulnerabilities (which we refer to as pharmacotypes) for a set of colon cancer PDX models. We find that a PDX model derived from a primary site tumor and a metastatic lesion from the same patient have different pharmacotypes. Using annotations of small molecule targets, we can identify proteins and signaling pathways that represent apoptotic vulnerabilities in colon PDX models. Finally, we apply HT-DBP to primary human thyroid tumors and sarcomas to identify potential active therapies. In sum, HT-DBP can efficiently predict therapeutic sensitivity upon short-term ex vivo drug exposure and may empower functional precision medicine approaches in the clinic.

Citation Format: Patrick D. Bhola, Eman Ahmed, Jennifer Guerriero, Ewa Sicinska, Emily Su, Jing Ni, Elizaveta Lavrova, Otari Chipashvili, Timothy Hagan, Kimmie Ng, Andrew Aguirre, Jochen Lorch, Suzanne George, George Demetri, Jean Zhao, Anthony Letai. High-throughput dynamic BH3 profiling identifies active cancer therapies in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4478.

Abstract 260: Identifying cancer drug sensitivity using live cell imaging dynamic BH3 profiling of solid tumor core biopsies

Given the rapid development of new small molecule cancer therapeutics, there is a growing need for predictive diagnostics to match cancer patients with optimal therapies. We previously developed a precision medicine technology with a functional phenotypic readout called dynamic BH3 profiling (DBP). DBP exposes cancer cells to drugs and measures induction of apoptotic cell death signaling after 24 hours ex vivo. Nonetheless, the application of DBP to core biopsies from metastatic tumors or other limited samples remains a technical challenge. Here, we adapt the DBP protocol for use on samples with small numbers of cells such as core biopsies. We maximize information returned per cell by imaging mitochondrial integrity in response to BH3 peptide exposure over time. We first show that the adapted protocol works in limited numbers of cancer cell lines, and in limited cells from the MMTV-PyMT genetically engineered mouse model of breast cancer. Specifically, we show that our ex vivo DBP predictions of the MMTV-PyMT mouse tumor matches known in vivo response. Finally, we apply our modified protocol to patient derived xenografts of colon cancer and primary patient colon tumors. We expect that our adapted protocol will find utility as a clinical biomarker, and as a method to optimize pre-clinical drug testing.

Citation Format: Rebecca German, Elizaveta Lavrova, Timothy Hagan, Otari Chipashvili, Ewa Sicinska, James Cleary, Kimmie Ng, Anthony Letai, Patrick Bhola. Identifying cancer drug sensitivity using live cell imaging dynamic BH3 profiling of solid tumor core biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 260.