Evaluating [225Ac]Ac-FAPI-46 for the treatment of soft-tissue sarcoma in mice.

PURPOSE: Fibroblast Activation Protein (FAP) is an emerging theranostic target that is highly expressed on cancer-associated fibroblasts and on certain tumor cells including sarcoma. We investigated the anti-tumor efficacy of [225Ac]Ac-FAPI-46 as monotherapy or in combination with immune checkpoint blockade (ICB) in immunocompetent murine models of sarcoma sensitive or resistant to ICB. METHODS: [68Ga]Ga- and [225Ac]Ac-FAPI-46 were tested in subcutaneous FAP+ FSA fibrosarcoma bearing C3H/Sed/Kam mice. The efficacy of up to three cycles of 60 kBq [225Ac]Ac-FAPI-46 was evaluated as monotherapy and in combination with an anti-PD-1 antibody. Efficacy of [225Ac]Ac-FAPI-46 and/or ICB was further compared in FAP-overexpressing FSA (FSA-F) tumors that were sensitive to ICB or rendered ICB-resistant by tumor-induction in the presence of Abatacept. RESULTS: [225Ac]Ac-FAPI-46 was well tolerated up to 3 × 60 kBq but had minimal effect on FSA tumor growth. The combination of three cycles [225Ac]Ac-FAPI-46 and ICB resulted in growth delay in 55% of mice (6/11) and partial tumor regression in 18% (2/11) of mice. In FSA-F tumors with FAP overexpression, both [225Ac]Ac-FAPI-46 and ICB were effective without additional benefits from the combination. In locally immunosuppressed and ICB resistant FAP-F tumors, however, [225Ac]Ac-FAPI-46 restored responsiveness to ICB, resulting in significant tumor regression and tumor-free survival of 56% of mice in the combination group up to 60 days post treatment. CONCLUSION: [225Ac]Ac-FAPI-46 efficacy is correlated with tumoral FAP expression levels and can restore responsiveness to PD-1 ICB. These data illustrate that careful patient selection based on target expression and rationally designed combination therapies are critically important to maximize the therapeutic impact of FAP-targeting radioligands.

Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging.

Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m2 ; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m2 ; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC and accuracy over SVM, and either model can be used as a faster OSA screening tool for subjects having brain DTI data.

Children's Hospital of Philadelphia Score to predict severe retinopathy in Indian preterm infants.

BACKGROUND: Retinopathy of Prematurity (ROP) screenings are expensive and entail heavy workload. Predictive models using postnatal weight gain reduces the number of ophthalmological examinations. The objective was to validate Children's Hospital of Philadelphia (CHOP) score to predict severe ROP in resource limited settings. METHODS: Prior to ophthalmic examination, the CHOP score was calculated to predict severe ROP (point estimate = 0.014) in 191 preterm infants. Cut-off point estimate, most suitable in resource limited settings was assessed. RESULTS: CHOP Score cutoff point (0.014) showed 67% sensitivity, 75% specificity. With CHOP score cut-off point (0.010), the corresponding values were 100% sensitivity, 51% specificity, PPV 12% and NPV 100%. CONCLUSION: CHOP Score (0.014) is a poor tool to predict the onset of severe ROP. However, CHOP Score (0.010) is a promising tool to predict the onset of severe ROP and reduces the need for ophthalmological examinations by 50% in resource limited settings.