Cabozantinib monotherapy for advanced adrenocortical carcinoma: a single-arm, phase 2 trial.

BACKGROUND: Adrenocortical carcinoma is a rare malignancy with poor response to systemic chemotherapy. Mitotane is the only approved therapy for adrenocortical carcinoma. Cabozantinib is a multikinase inhibitor approved in multiple malignancies. This is the first prospective trial to explore the anti-tumour activity, safety, and pharmacokinetic profile of cabozantinib in patients with advanced adrenocortical carcinoma. METHODS: This investigator-initiated, single-arm, phase 2 trial in adult patients (aged ≥18 years) with advanced adrenocortical carcinoma was done at the University of Texas MD Anderson Cancer Center (Houston, TX, USA). Eligible patients had histologically confirmed adrenocortical carcinoma, were not candidates for surgery with curative intent, had measurable disease, had an estimated life expectancy of at least 3 months, and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 with adequate organ function. Patients who had used mitotane within 6 months of study participation were required to have a serum mitotane level of less than 2 mg/L. Patients were given oral cabozantinib 60 mg daily with the option of dose reduction to manage adverse events. The primary endpoint was progression-free survival at 4 months, assessed in all patients who received at least one dose of study drug per protocol. This study is registered with ClinicalTrials.gov, NCT03370718, and is now complete. FINDINGS: Between March 1, 2018, and May 31, 2021, we enrolled 18 patients (ten males and eight females), all of whom received at least one dose of study treatment. Of the 18 patients, eight (44%) had an ECOG performance status of 0, nine (50%) patients had a performance status of 1, and one (6%) patient had a performance status of 2. Median follow-up was 36·8 months (IQR 30·2-50·3). At 4 months, 13 (72·2%; 95% CI 46·5-90·3) of 18 patients had progression-free survival and median progression-free survival was 6 months (95% CI 4·3 to not reached). One patient remains on treatment. Treatment-related adverse events of grade 3 or worse occurred in 11 (61%) of 18 patients. The most common grade 3 adverse events were lipase elevation (three [17%] of 18 patients), elevated γ-glutamyl transferase concentrations (two [11%] patients), elevated alanine aminotransferase concentrations (two [11%] patients), hypophosphatemia (two [11%] patients), and hypertension (two [11%] patients). One (6%) of 18 patients had grade 4 hypertension. No treatment related deaths occurred on study. INTERPRETATION: Cabozantinib in advanced adrenocortical carcinoma showed promising efficacy with a manageable and anticipated safety profile. Further prospective studies with cabozantinib alone and in combination with immune checkpoint therapy are ongoing. FUNDING: Exelixis.

A novel method to guide biomarker combinations to optimize the sensitivity.

Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore, the application of logistic regression is limited in such settings. To this end, we have developed an improved regression framework, SMAGS, for binary classification that, for a given specificity, finds the linear decision rule that yields the maximum sensitivity. Furthermore, we employed the method for feature selection to find the features that are satisfying the sensitivity maximization goal. We compared our method with normal logistic regression by applying it to real clinical data as well as synthetic data. In the real application data (colorectal cancer dataset), we found 14% improvement of sensitivity at 98.5% specificity. Availability and implementation: Software is made available in Python ( https://github.com/smahmoodghasemi/SMAGS ).

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. PURPOSE: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. METHODS: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. RESULTS: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. CONCLUSIONS: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Estimating causal effects with hidden confounding using instrumental variables and environments.

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.