Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). METHODS: This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. RESULTS: Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). CONCLUSIONS: The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.

Prospective Deployment of Deep Learning Reconstruction Facilitates Highly Accelerated Upper Abdominal MRI.

RATIONALE AND OBJECTIVES: To compare a conventional T1 volumetric interpolated breath-hold examination (VIBE) with SPectral Attenuated Inversion Recovery (SPAIR) fat saturation and a deep learning (DL)-reconstructed accelerated VIBE sequence with SPAIR fat saturation achieving a 50 % reduction in breath-hold duration (hereafter, VIBE-SPAIRDL) in terms of image quality and diagnostic confidence. MATERIALS AND METHODS: This prospective study enrolled consecutive patients referred for upper abdominal MRI from November 2023 to December 2023 at a single tertiary center. Patients underwent upper abdominal MRI with acquisition of non-contrast and gadobutrol-enhanced conventional VIBE-SPAIR (fourfold acceleration, acquisition time 16 s) and VIBE-SPAIRDL (sixfold acceleration, acquisition time 8 s) on a 1.5 T scanner. Image analysis was performed by four readers, evaluating homogeneity of fat suppression, perceived signal-to-noise ratio (SNR), edge sharpness, artifact level, lesion detectability and diagnostic confidence. A statistical power analysis for patient sample size estimation was performed. Image quality parameters were compared by a repeated measures analysis of variance, and interreader agreement was assessed using Fleiss' κ. RESULTS: Among 450 consecutive patients, 45 patients were evaluated (mean age, 60 years ± 15 [SD]; 27 men, 18 women). VIBE-SPAIRDL acquisition demonstrated superior SNR (P < 0.001), edge sharpness (P < 0.001), and reduced artifacts (P < 0.001) with substantial to almost perfect interreader agreement for non-contrast (κ: 0.70-0.91) and gadobutrol-enhanced MRI (κ: 0.68-0.87). No evidence of a difference was found between conventional VIBE-SPAIR and VIBE-SPAIRDL regarding homogeneity of fat suppression, lesion detectability, or diagnostic confidence (all P > 0.05). CONCLUSION: Deep learning reconstruction of VIBE-SPAIR facilitated a reduction of breath-hold duration by half, while reducing artifacts and improving image quality. SUMMARY: Deep learning reconstruction of prospectively accelerated T1 volumetric interpolated breath-hold examination for upper abdominal MRI enabled a 50 % reduction in breath-hold time with superior image quality. KEY RESULTS: 1) In a prospective analysis of 45 patients referred for upper abdominal MRI, accelerated deep learning (DL)-reconstructed VIBE images with spectral fat saturation (SPAIR) showed better overall image quality, with better perceived signal-to-noise ratio and less artifacts (all P < 0.001), despite a 50 % reduction in acquisition time compared to conventional VIBE. 2) No evidence of a difference was found between conventional VIBE-SPAIR and accelerated VIBE-SPAIRDL regarding lesion detectability or diagnostic confidence.

Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers.

Neuroendocrine neoplasms (NENs) are a diverse group of tumors with varying clinical behaviors. Their incidence has risen due to increased awareness, improved diagnostics, and aging populations. The 2019 World Health Organization classification emphasizes integrating radiology and histopathology to characterize NENs and create personalized treatment plans. Imaging methods like CT, MRI, and PET/CT are crucial for detection, staging, treatment planning, and monitoring, but each of them poses different interpretative challenges and none are immune to pitfalls. Treatment options include surgery, targeted therapies, and chemotherapy, based on the tumor type, stage, and patient-specific factors. This review aims to provide insights into the latest developments and challenges in NEN imaging, diagnosis, and management.

Diagnostic Anatomic Imaging for Neuroendocrine Neoplasms: Maximizing Strengths and Mitigating Weaknesses.

ABSTRACT: Neuroendocrine neoplasms are a heterogeneous group of gastrointestinal and lung tumors. Their diverse clinical manifestations, variable locations, and heterogeneity present notable diagnostic challenges. This article delves into the imaging modalities vital for their detection and characterization. Computed tomography is essential for initial assessment and staging. At the same time, magnetic resonance imaging (MRI) is particularly adept for liver, pancreatic, osseous, and rectal imaging, offering superior soft tissue contrast. The article also highlights the limitations of these imaging techniques, such as MRI's inability to effectively evaluate the cortical bone and the questioned cost-effectiveness of computed tomography and MRI for detecting specific gastric lesions. By emphasizing the strengths and weaknesses of these imaging techniques, the review offers insights into optimizing their utilization for improved diagnosis, staging, and therapeutic management of neuroendocrine neoplasms.

Imaging of Neuroendocrine Neoplasms; Principles of Treatment Strategies. What Referring Clinicians Want to Know.

ABSTRACT: Neuroendocrine neoplasms (NENs) are a diverse group of tumors that express neuroendocrine markers and primarily affect the lungs and digestive system. The incidence of NENs has increased over time due to advancements in imaging and diagnostic techniques. Effective management of NENs requires a multidisciplinary approach, considering factors such as tumor location, grade, stage, symptoms, and imaging findings. Treatment strategies vary depending on the specific subtype of NEN. In this review, we will focus on treatment strategies and therapies including the information relevant to clinicians in order to undertake optimal management and treatment decisions, the implications of different therapies on imaging, and how to ascertain their possible complications and treatment effects.

Clinical Intricacies and Advances in Neuroendocrine Tumors: An Organ-Based Multidisciplinary Approach.

ABSTRACT: Neuroendocrine neoplasms (NENs) are rare neoplasms originating from neuroendocrine cells, with increasing incidence due to enhanced detection methods. These tumors display considerable heterogeneity, necessitating diverse management strategies based on factors like organ of origin and tumor size. This article provides a comprehensive overview of therapeutic approaches for NENs, emphasizing the role of imaging in treatment decisions. It categorizes tumors based on their locations: gastric, duodenal, pancreatic, small bowel, colonic, rectal, appendiceal, gallbladder, prostate, lung, gynecological, and others. The piece also elucidates the challenges in managing metastatic disease and controversies surrounding MEN1-neuroendocrine tumor management. The article underscores the significance of individualized treatment plans, underscoring the need for a multidisciplinary approach to ensure optimal patient outcomes.

State-of-the-Art Hybrid Imaging of Neuroendocrine Neoplasms.

ABSTRACT: Neuroendocrine neoplasms (NENs) may be challenging to diagnose due to their small size and diverse anatomical locations. Hybrid imaging techniques, specifically positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI), represent the current state-of-the-art for evaluating NENs. The preferred radiopharmaceuticals for NEN PET imaging are gallium-68 (68Ga) DOTA-peptides, which target somatostatin receptors (SSTR) overexpressed on NEN cells. Clinical applications of [68Ga]Ga-DOTA-peptides PET/CT include diagnosis, staging, prognosis assessment, treatment selection, and response evaluation. Fluorodeoxyglucose-18 (18F-FDG) PET/CT aids in detecting low-SSTR-expressing lesions and helps in patient stratification and treatment planning, particularly in grade 3 neuroendocrine tumors (NETs). New radiopharmaceuticals such as fluorine-labeled SSTR agonists and SSTR antagonists are emerging as alternatives to 68Ga-labeled peptides, offering improved detection rates and favorable biodistribution. The maturing of PET/MRI brings advantages to NEN imaging, including simultaneous acquisition of PET and MRI images, superior soft tissue contrast resolution, and motion correction capabilities. The PET/MRI with [68Ga]Ga-DOTA-peptides has demonstrated higher lesion detection rates and more accurate lesion classification compared to PET/CT. Overall, hybrid imaging offers valuable insights in the diagnosis, staging, and treatment planning of NENs. Further research is needed to refine response assessment criteria and standardize reporting guidelines.

An Illustrated Review of the Recent 2019 World Health Organization Classification of Neuroendocrine Neoplasms: A Radiologic and Pathologic Correlation.

ABSTRACT: Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Non-invasive estimation of split renal function from routine 68Ga-SSR-PET/CT scans.

Objective: Patients with impaired kidney function are at elevated risk for nephrotoxicity and hematotoxicity from peptide receptor radionuclide therapy (PPRT) for advanced neuroendocrine tumors. Somatostatin receptor (SSR)-PET/CT imaging is the method of choice to identify sufficient SSR expression as a prerequisite for PRRT. Therefore, our study aimed to explore whether split renal function could be evaluated using imaging data from routine SSR-PET/CT prior to PRRT. Methods: In total, 25 consecutive patients who underwent SSR-PET/CT (Siemens Biograph mCT®) before PRRT between June 2019 and December 2020 were enrolled in this retrospective study. PET acquisition in the caudocranial direction started at 20 ± 0.5 min after an i.v. injection of 173 ± 20 MBq [68Ga]Ga-ha DOTATATE, and the kidneys were scanned at 32 ± 0.5 min p.i. The renal parenchyma was segmented semi-automatically using an SUV-based isocontour (SUV between 5 and 15). Multiple parameters including SUVmean of renal parenchyma and blood pool, as well as parenchyma volume, were extracted, and accumulation index (ACI: renal parenchyma volume/SUVmean) and total kidney accumulation (TKA: SUVmean x renal parenchyma volume) were calculated. All data were correlated with the reference standard tubular extraction rate (TER-MAG) from [99mTc]Tc-MAG3 scintigraphy and glomerular filtration rate (GFRCDK - EPI). Results: SUVmean of the parenchymal tracer retention showed a negative correlation with TERMAG (r: -0.519, p < 0.001) and GFRCDK - EPI (r: -0.555, p < 0.001) at 32 min p.i. The herein-introduced ACI revealed a significant correlation (p < 0.05) with the total tubular function (r: 0.482), glomerular renal function (r: 0.461), split renal function (r: 0.916), and absolute single-sided renal function (r: 0.549). The mean difference between the split renal function determined by renal scintigraphy and ACI was 1.8 ± 4.2 % points. Conclusion: This pilot study indicates that static [68Ga]Ga-ha DOTATATE PET-scans at 32 min p.i. may be used to estimate both split renal function and absolute renal function using the herein proposed "Accumulation Index" (ACI).

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging.

PURPOSE: Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality. METHOD: In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics. RESULTS: After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction. CONCLUSIONS: An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.

[Multiparametric MRI of the prostate: requirements and principles regarding diagnostic reporting]. / Die multiparametrische MRT der Prostata: Anforderungen und Grundlagen der Befundung.

Multiparametric MRI (mpMRI) is one of the primary diagnostic tools for detecting clinically relevant prostate cancer. It should be routinely used in addition to urological investigations owing to its higher diagnostic yield than systematic biopsies. However, combining targeted and systematic biopsies achieves the highest diagnostic rate. The Prostate Imaging Reporting and Data System (PI-RADS Version 2.1) standardizes the acquisition and interpretation of mpMRI of the prostate. It consists of high-resolution T2- and diffusion-weighted images, the corresponding apparent diffusion coefficient (ADC) maps, and a dynamic contrast-enhanced sequence. Reports describe the increasing likelihood of clinically significant prostate cancer with PI-RADS categories 1-5. The MRI sequence determining the PI-RADS category of a lesion depends on its location within the prostate: in the transitional zone, the T2-weighted sequence and, in the peripheral zone, the diffusion-weighted sequence are the primary determinants. The diffusion-weighted and contrast-enhanced sequences provide secondary classification for the transitional and peripheral zones, respectively. This review summarizes and illustrates the diagnostic criteria defined in PI-RADS 2.1. In addition, evidence for mpMRI of the prostate, its indication and implementation are described.

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study.

Background: High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. Methods: In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. Results: Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. Conclusions: We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

Multiparametric Dual-Time-Point [18F]FDG PET/MRI for Lymph Node Staging in Patients with Untreated FIGO I/II Cervical Carcinoma.

[18F]FDG PET/MRI was shown to have limited sensitivity for N-staging in FIGO I/II cervical carcinoma. Therefore, this prospective study aimed to investigate the additional value of multiparametric dual-time-point PET/MRI and to assess potential influencing factors for lymph node metastasis (LNM) detection. A total of 63 patients underwent whole-body dual-time-point [18F]FDG PET/MRI 60 + 90 min p.i., and 251 LN were evaluated visually, quantified multiparametrically, and correlated with histology. Grading of the primary tumor (G2/G3) had a significant impact on visual detection (sens: 8.3%/31%). The best single parameter for LNM detection was SUVavg, however, with a significant loss of discriminatory power in G2 vs. G3 tumors (AUC: 0.673/0.901). The independent predictors SUVavg, ∆SUVpeak, LN sphericity, ADC, and histologic grade were included in the logistic-regression-based malignancy score (MS) for multiparametric analysis. Application of MS enhanced AUCs, especially in G2 tumors (AUC: G2:0.769; G3:0.877) and improved the accuracy for single LNM from 34.5% to 55.5% compared with the best univariate parameter SUVavg. Compared with visual analysis, the use of the malignancy score increased the overall sensitivity from 31.0% to 79.3% (Youden optimum) with a moderate decrease in specificity from 98.3% to 75.6%. These findings indicate that multiparametric evaluation of dual-time-point PET/MRI has the potential to improve accuracy compared with visual interpretation and enables sufficient N-staging also in G2 cervical carcinoma.

Photoacoustic imaging radiomics in patient-derived xenografts: a study on feature sensitivity and model discrimination.

Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation status and potentially treatment response assessment. Currently, the measurements most commonly performed on such images are the mean and median of the pixel values of the tumour volumes of interest. We investigated expanding the set of measurements that can be extracted from these images by adding radiomic features. In particular, we found that Skewness was sensitive to differences between basal and luminal patient derived xenograft cancer models with an [Formula: see text] of 0.86, and that it was robust to variations in confounding factors such as reconstruction type and wavelength. We also built discriminant models with radiomic features that were correlated with the underlying tumour model and were independent from each other. We then ranked features by their importance in the model. Skewness was again found to be an important feature, as were 10th Percentile, Root Mean Squared, and several other texture-based features. In summary, this paper proposes a methodology to select radiomic features extracted from photoacoustic images that are robust to changes in acquisition and reconstruction parameters, and discusses features found to have discriminating power between the underlying tumour models in a pre-clinical dataset.

Imaging Glioblastoma Metabolism by Using Hyperpolarized [1-13C]Pyruvate Demonstrates Heterogeneity in Lactate Labeling: A Proof of Principle Study.

Purpose To evaluate glioblastoma (GBM) metabolism by using hyperpolarized carbon 13 (13C) MRI to monitor the exchange of the hyperpolarized 13C label between injected [1-13C]pyruvate and tumor lactate and bicarbonate. Materials and Methods In this prospective study, seven treatment-naive patients (age [mean ± SD], 60 years ± 11; five men) with GBM were imaged at 3 T by using a dual-tuned 13C-hydrogen 1 head coil. Hyperpolarized [1-13C]pyruvate was injected, and signal was acquired by using a dynamic MRI spiral sequence. Metabolism was assessed within the tumor, in the normal-appearing brain parenchyma (NABP), and in healthy volunteers by using paired or unpaired t tests and a Wilcoxon signed rank test. The Spearman ρ correlation coefficient was used to correlate metabolite labeling with lactate dehydrogenase A (LDH-A) expression and some immunohistochemical markers. The Benjamini-Hochberg procedure was used to correct for multiple comparisons. Results The bicarbonate-to-pyruvate (BP) ratio was lower in the tumor than in the contralateral NABP (P < .01). The tumor lactate-to-pyruvate (LP) ratio was not different from that in the NABP (P = .38). The LP and BP ratios in the NABP were higher than those observed previously in healthy volunteers (P < .05). Tumor lactate and bicarbonate signal intensities were strongly correlated with the pyruvate signal intensity (ρ = 0.92, P < .001, and ρ = 0.66, P < .001, respectively), and the LP ratio was weakly correlated with LDH-A expression in biopsy samples (ρ = 0.43, P = .04). Conclusion Hyperpolarized 13C MRI demonstrated variation in lactate labeling in GBM, both within and between tumors. In contrast, bicarbonate labeling was consistently lower in tumors than in the surrounding NABP. Keywords: Hyperpolarized 13C MRI, Glioblastoma, Metabolism, Cancer, MRI, Neuro-oncology Supplemental material is available for this article. Published under a CC BY 4.0 license.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.

Background: Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. Methods: Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). Results: The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. Conclusions: CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

A Phase II study of neoadjuvant axitinib for reducing the extent of venous tumour thrombus in clear cell renal cell cancer with venous invasion (NAXIVA).

BACKGROUND: Surgery for renal cell carcinoma (RCC) with venous tumour thrombus (VTT) extension into the renal vein (RV) and/or inferior vena cava (IVC) has high peri-surgical morbidity/mortality. NAXIVA assessed the response of VTT to axitinib, a potent tyrosine kinase inhibitor. METHODS: NAXIVA was a single-arm, multi-centre, Phase 2 study. In total, 20 patients with resectable clear cell RCC and VTT received upto 8 weeks of pre-surgical axitinib. The primary endpoint was percentage of evaluable patients with VTT improvement by Mayo level on MRI. Secondary endpoints were percentage change in surgical approach and VTT length, response rate (RECISTv1.1) and surgical morbidity. RESULTS: In all, 35% (7/20) patients with VTT had a reduction in Mayo level with axitinib: 37.5% (6/16) with IVC VTT and 25% (1/4) with RV-only VTT. No patients had an increase in Mayo level. In total, 75% (15/20) of patients had a reduction in VTT length. Overall, 41.2% (7/17) of patients who underwent surgery had less invasive surgery than originally planned. Non-responders exhibited lower baseline microvessel density (CD31), higher Ki67 and exhausted or regulatory T-cell phenotype. CONCLUSIONS: NAXIVA provides the first Level II evidence that axitinib downstages VTT in a significant proportion of patients leading to reduction in the extent of surgery. CLINICAL TRIAL REGISTRATION: NCT03494816.