Caudal regression syndrome type 1 with minimally invasive computed tomography and magnetic resonance imaging autopsy: a case report.

BACKGROUND: Caudal regression syndrome is a rare complex congenital anomaly with reduced penetrance and phenotypic variability characterized by osseous defects of the caudal spine, lower limb anomalies, and accompanying genitourinary, gastrointestinal/anorectal, and cardiac system soft tissue defects. We report a rare presentation of type 1 caudal regression syndrome in a pregnant woman with preexisting diabetes, in which early recognition of severe fetal anomalies on routine antenatal ultrasound facilitated confirmation with fetal magnetic resonance imaging to characterize extent of disease and prognosticate fetal outcome. CASE PRESENTATION: This case of type 1 caudal regression syndrome in the setting of maternal pregestational diabetes mellitus resulted in stillbirth. The mother was a 29-year-old Caucasian primigravida female with past medical history of poorly controlled type 2 diabetes managed with metformin prior to pregnancy, prompting admission for glucose management and initiation of insulin at 13 weeks. Baseline hemoglobin A1c was high at 8.0%. Fetal ultrasound at 22 weeks was notable for severe sacral agenesis, bilateral renal pelvis dilatation, single umbilical artery, and pulmonary hypoplasia. Fetal magnetic resonance imaging at 29 weeks showed absent lower two-thirds of the spine with corresponding spinal cord abnormality compatible with type 1 caudal regression syndrome. The mother delivered a male stillborn at 39 and 3/7 weeks. Minimally invasive postmortem magnetic resonance imaging and computed tomography autopsy were performed to confirm clinical findings when family declined conventional autopsy. Etiology of sacral agenesis was attributed to poorly controlled maternal diabetes early in gestation. CONCLUSION: Maternal preexisting diabetes is a known risk factor for development of congenital malformations. This rare case of type 1 caudal regression syndrome in a mother with preexisting diabetes with elevated hemoglobin A1c highlights the importance of preconception glycemic control in diabetic women and the utility of fetal magnetic resonance imaging for confirmation of ultrasound findings to permit accurate prognostication. Additionally, minimally invasive postmortem magnetic resonance imaging and computed tomography autopsy can facilitate diagnostic confirmation of clinical findings in perinatal death due to complex congenital anomalies while limiting the emotional burden on bereaved family members who decline conventional autopsy.

Author response to Cunha et al.

The need to identify biomarkers to predict immunotherapy response for rare cancers has been long overdue. We aimed to study this in our paper, 'Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers'. In this response to the Letter to the Editor by Cunha et al, we explain and discuss the reasons behind choosing LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) with LOOCV (Leave-One-Out Cross-Validation) as the feature selection and classifier method, respectively for our radiomics models. Also, we highlight what care was taken to avoid any overfitting on the models. Further, we checked for the multicollinearity of the features. Additionally, we performed 10-fold cross-validation instead of LOOCV to see the predictive performance of our radiomics models.

Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers.

BACKGROUND: We present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers. METHODS: The study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 "controlled disease" (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance. FINDINGS: The 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; p<0.001) and in patients assessed by irRECIST (94.7%, 93.9%, and 95.8%, respectively; p<0.001). Additionally, the common features of the RECIST and irRECIST groups also highly predicted pembrolizumab response with accuracy, sensitivity, specificity, and p value of 94.7%, 97%, 90%, p<0.001% and 96%, 96%, 95%, p<0.001, respectively. CONCLUSION: Our radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer. INTERPRETATION: Our radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

Cancer Imaging in Immunotherapy.

Immune therapeutics are revolutionizing cancer treatments. In tandem, new and confounding imaging characteristics have appeared that are distinct from those typically seen with conventional cytotoxic therapies. In fact, only 10% of patients on immunotherapy may show tumor shrinkage, typical of positive responses on conventional therapy. Conversely, those on immune therapies may initially demonstrate a delayed response, transient enlargement followed by tumor shrinkage, stable size, or the appearance of new lesions. Response Evaluation Criteria in Solid Tumors (RECIST) or WHO criteria, developed to identify early effects of cytotoxic agents, may not provide a complete evaluation of new emerging treatment response pattern of immunotherapeutic agents. Therefore, new imaging response criteria, such as the immune-related Response Evaluation Criteria in Solid Tumors (irRECIST), immune Response Evaluation Criteria in Solid Tumors (iRECIST), and immune-related Response Criteria (irRC), are proposed. However, FDA approval of emerging therapies including immunotherapies still relies on the current RECIST criteria. In this chapter, we review the traditional and new imaging response criteria for evaluation of solid tumors and briefly touch on some of the more commonly associated immunotherapy-induced adverse events.

Cancer Imaging in Immunotherapy.

Immune therapeutics are revolutionizing cancer treatments. In tandem, new and confounding imaging characteristics have appeared that are distinct from those typically seen with conventional cytotoxic therapies. In fact, only 10% of patients on immunotherapy may show tumor shrinkage, typical of positive responses on conventional therapy. Conversely, those on immune therapies may initially demonstrate a delayed response, transient enlargement followed by tumor shrinkage, stable size, or the appearance of new lesions. New imaging response criteria, such as the immune-related Response Evaluation Criteria in Solid Tumors (irRECIST) and immune-related Response Criteria (irRC), are being implemented in many trials. However, FDA approval of emerging therapies, including immunotherapies, still relies on the current RECIST criteria. In this chapter, we review the traditional and new imaging response criteria for evaluation of solid tumors and briefly touch on some of the more commonly associated immunotherapy-induced adverse events.