Anti-GD2 Based Immunotherapy Prevents Late Events in High-Risk Neuroblastoma Patients over 18 Months at Diagnosis

Background: Anti-GD2 based immunotherapy has improved overall (OS) and event free survival (EFS) for high-risk neuroblastoma (HR-NBL) patients. Here, we evaluate the long-term efficacy of anti-GD2 immunotherapy in combination with isotretinoin, GM-CSF, and IL-2. Methods: Dutch HR-NBL patients treated with immunotherapy according to the COG-ANBL0032 protocol (n = 47) were included and compared to historical controls (n = 37) treated with single-agent isotretinoin maintenance therapy. Survival time was calculated from start of the maintenance therapy. Results: The study and control group were similar concerning baseline characteristics. In the complete cohort, 5 year OS was 64±7% and 49±8% for the immunotherapy group and the control group, respectively (p = 0.16). Five year EFS was 57±7% and 41±8%, respectively (p = 0.16). In the subgroup of patients ≥ 18 months, 5-yr OS was 63±8% and 39±9, respectively (p = 0.04) and EFS 54±8% and 29±8%, respectively (p = 0.05). Landmark analysis for EFS with landmark point at 6 months after start of maintenance suggests a larger effect on the prevention of late than early events. Conclusions: This study is the first to confirm the results of the COG-ANBL0032 study in a cohort treated with a different induction regimen. Anti-GD2 immunotherapy prevents late events, most significantly in patients older than 18 months of age at diagnosis.

PENERAPAN FACIAL LANDMARK POINT UNTUK KLASIFIKASI JENIS KELAMIN BERDASARKAN CITRA WAJAH

Teknologi biometrik yang biasa digunakan adalah face recognition yang menganalisa wajah manusia sehingga menghasilkan suatu informasi salah satunya adalah jenis kelamin. Komputer perlu diberikan pembelajaran untukmembedakan jenis kelamin berdasarkan komponen wajah. Pembelajaran mengenai deteksi jenis kelamin memiliki beberapa kesulitan karena kompleksitas dari kondisi wajah, seperti posisi gambar, pencahayaan dan ekspresi.Beberapa penelitian, menyimpulkan bahwa fitur geometri merupakan fitur yang cocok untuk mengatasi kompleksitas tersebut. Pada penelitian ini, fitur yang digunakan adalah jarak antara kedua mata, lebar nasal root, lebar hidung, tinggi bibir atas dan tinggi bibir bawah. Pengukuran nilai-nilai pada fitur tersebut dapat dilakukan dengan bantuan landmark point seperti dlib regression tree. Setelah itu nilai-nilai yang didapatkan digunakan untuk perhitungan klasifikasi menggunakan metode Support Vector Machine dengan kernel Gaussian RBF dan normalisasi Sigmoid. Menggunakan dataset sejumlah 60 data jenis A dan B untuk training dan 20 untuk testing, hasil akurasi yang didapatkan adalah 70%, precision 80%, dan recall 67%. Sedangkan dengan dataset sejumlah 30 data jenis A untuk training, hasil akurasi yang didapatkan adalah 40%, precision 30%, dan recall 37.5%. Sedangankan dengan dataset sejumlah 30 data jenis B untuk training, hasil akurasi yang didapatkan adalah 65%, precision 70%, dan recall 64%.

In-Motion Iterative Fine Alignment Algorithm for On-Board Vehicular Odometer-Aided SINS

This research proposes a novel in-motion fine alignment algorithm for vehicular dead reckoning (DR) with odometer-aided strapdown inertial navigation system (SINS) while the map matching result is used for a group of landmark points to estimate misalignment angles. The proposed algorithm is designed based on principle of similarity, that is, trajectory of DR is similar to the true trajectory that the main difference between these two trajectories is rotation and scale. Further, the results from map matching are introduced as a group of landmark points to estimate the residual of azimuth error angle after coarse alignment and the scale factor error of the odometer. It is theoretically proved that the alignment effectiveness based on the results from map matching is equivalent to that on single zero error landmark point. Finally, digital simulations are conducted to verify the presented algorithm and test the performance.