Konsepsi Penggantian Kerugian Atas Pemberian Izin Mendirikan Bangunan (Imb) yang Tidak Sesuai dengan Rtrw (Kajian terhadap Pasal 37 Undang-undang No.26 Tahun 2007 Tentang Penataan Ruang)

Pasal 37 Ayat (4),(5) dan (8) Undang-undang No.26 Tahun 2007 Tentang Penataan Ruang mengatakan bahwa IMB harus mengikuti konsep perencanaan yang tertera pada Rencana Tata Ruang Wilayah (RTRW) di setiap daerah dan apabila diketahui IMB tersebut melanggar RTRW maka harus dibatalkan dan dimungkinkan adanya pemberian ganti rugi atas pembatalan IMB tersebut. Fenomena yang terjadi saat ini adalah belum jelas dan belum konkretnya aturan yang ada terkait dengan konsepsi ganti rugi sehingga menyulitkan pihak-pihak yang ingin mengajukan upaya hukum melalui sarana hukum yang paling tepat dan efisien. Berdasarkan penelitian ini, penulis menawarkan sarana hukum administrasi karena dianggap yang paling efektif dan jelas dalam menyelesaikan permasalahan IMB yang dilakukan pembatalan, dikarenakan IMB merupakan Keputusan Tata Usaha Negara (KTUN) yang apabila bermasalah sudah terakomodasi di Pengadilan Tata Usaha Negara, sesuai dengan kompetensinya dan yang paling penting adalah gugatan yang dilakukan, terhadap subjek kewenangan yaitu pejabatnya bukan pribadi dari pejabat tersebut yang bertanggung jawab terhadap kesalahan-kesalahan yang berkaitan dengan ketidaksesuaiannya IMB dengan RTRW. Maka, penulis mengusulkan konsepsi penggantian atas kerugian yang diderita oleh investor atau masyarakat dengan melalui mekanisme penggantian yang dibebankan pada pemerintah daerah melalui Anggaran Pendapatan dan Belanja Daerah (APBD). Sehingga diharapkan dapat mengembalikan hakikat tujuan dan manfaat dari IMB.Kata Kunci : Izin Mendirikan Bangunan (IMB), Rencana Tata Ruang Wilayah (RTRW), Upaya Hukum Administrasi, Ganti Rugi

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images

[Purpose] The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment. [Methods] Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50 Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14–18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. [Results] The median GTV volume and motion range were 7.42 (range, 1.18–25.74) cm³ and 22 (range, 11–28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85 ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66–2.95) mm. The probability of the 3D deviation smaller than 5 mm was 93.6%. [Conclusions] The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer

Reducing variability among treatment machines using knowledge‐based planning for head and neck, pancreatic, and rectal cancer

PURPOSE: This study aimed to assess dosimetric indices of RapidPlan model-based plans for different energies (6, 8, 10, and 15 MV; 6- and 10-MV flattening filter-free), multileaf collimator (MLC) types (Millennium 120, High Definition 120, dual-layer MLC), and disease sites (head and neck, pancreatic, and rectal cancer) and compare these parameters with those of clinical plans. METHODS: RapidPlan models in the Eclipse version 15.6 were used with the data of 28, 42, and 20 patients with head and neck, pancreatic, and rectal cancer, respectively. RapidPlan models of head and neck, pancreatic, and rectal cancer were created for TrueBeam STx (High Definition 120) with 6 MV, TrueBeam STx with 10-MV flattening filter-free, and Clinac iX (Millennium 120) with 15 MV, respectively. The models were used to create volumetric-modulated arc therapy plans for a 10-patient test dataset using all energy and MLC types at all disease sites. The Holm test was used to compare multiple dosimetric indices in different treatment machines and energy types. RESULTS: The dosimetric indices for planning target volume and organs at risk in RapidPlan model-based plans were comparable to those in the clinical plan. Furthermore, no dose difference was observed among the RapidPlan models. The variability among RapidPlan models was consistent regardless of the treatment machines, MLC types, and energy. CONCLUSIONS: Dosimetric indices of RapidPlan model-based plans appear to be comparable to the ones based on clinical plans regardless of energies, MLC types, and disease sites. The results suggest that the RapidPlan model can generate treatment plans independent of the type of treatment machine

Multi-institutional phase II study on the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy for lung tumors

Background and purpose: This study aimed to evaluate the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy (DTT-SBRT) for lung tumors. Materials and methods: Patients with cStage I primary lung cancer or metastatic lung cancer with an expected range of respiratory motion of ≥10 mm were eligible for the study. The prescribed dose was 50 Gy in four fractions. A gimbal-mounted linac was used for DTT-SBRT delivery. The primary endpoint was local control at 2 years. Results: Forty-eight patients from four institutions were enrolled in this study. Forty-two patients had primary non-small-cell lung cancer, and six had metastatic lung tumors. DTT-SBRT was delivered for 47 lesions in 47 patients with a median treatment time of 28 min per fraction. The median respiratory motion during the treatment was 13.7 mm (range: 4.5–28.1 mm). The motion-encompassing method was applied for the one remaining patient due to the poor correlation between the abdominal wall and tumor movement. The median follow-up period was 32.3 months, and the local control at 2 years was 95.2% (lower limit of the one-sided 85% confidence interval [CI]: 90.3%). The overall survival and progression-free survival at 2 years were 79.2% (95% CI: 64.7%–88.2%) and 75.0% (95% CI: 60.2%–85.0%), respectively. Grade 3 toxicity was observed in one patient (2.1%) with radiation pneumonitis. Grade 4 or 5 toxicity was not observed. Conclusion: DTT-SBRT achieved excellent local control with low incidences of severe toxicities in lung tumors with respiratory motion

Autoregressive hidden semi-Markov model of symbolic music performance for score following

International audienceA stochastic model of symbolic (MIDI) performance of polyphonic scores is presented and applied to score following. Stochastic modelling has been one of the most successful strategies in this field. We describe the performance as a hierarchical process of performer's progression in the score and the production of performed notes, and represent the process as an extension of the hidden semi-Markov model. The model is compared with a previously studied model based on hidden Markov model (HMM), and reasons are given that the present model is advantageous for score following especially for scores with trills, tremolos, and arpeggios. This is also confirmed empirically by comparing the accuracy of score following and analysing the errors. We also provide a hybrid of this model and the HMM-based model which is computationally more efficient and retains the advantages of the former model. The present model yields one of the state-of-the-art score following algorithms for symbolic performance and can possibly be applicable for other music recognition problems

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

[Background] In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion. [Methods] From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems. [Results] The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively. [Conclusions] The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset

Programs for calculating the statistical powers of detecting susceptibility genes in case–control studies based on multistage designs

Motivation: A two-stage association study is the most commonly used method among multistage designs to efficiently identify disease susceptibility genes. Recently, some SNP studies have utilized more than two stages to detect disease genes. However, there are few available programs for calculating statistical powers and positive predictive values (PPVs) of arbitrary n-stage designs

Dosimetric Comparison between Dynamic Wave Arc and Co-Planar Volumetric Modulated Radiotherapy for Locally Advanced Pancreatic Cancer

Introduction: Dose reduction to the duodenum is important to decrease gastrointestinal toxicities in patients with locally advanced pancreatic cancer (LAPC) treated with definitive chemoradiotherapy. We aimed to compare dynamic wave arc (DWA), a volumetric-modulated beam delivery technique with simultaneous gantry/ring rotations passing the waved trajectories, with coplanar VMAT (co-VMAT) with respect to dose distributions in LAPC cases. Material and Methods: DWA and co-VMAT plans were created for 13 patients with LAPC. The prescribed dose was 45.6 or 48 Gy in 15 fractions. The dose volume indices (DVIs) for target volumes and organs at risk were compared between the corresponding plans. Gamma passing rate, monitor unit (MU), and beam-on time were also compared. Results: DWA significantly reduced the duodenal V39Gy, V42Gy, and V45Gy by 1.1, 0.8, and 0.2 cm3, and increased the liver mean dose and D2cm3 of the spinal cord planning volume by 1.0 and 1.5 Gy, respectively. Meanwhile, there was no significant difference in the target volumes except for D2% of PTV (111.5% in DWA vs. 110.5% in co-VMAT). Further, the gamma passing rate was similar in both plans. MU and beam-on time increased in DWA by 31 MUs and 15 seconds, respectively. Conclusion: DWA generated significantly lower duodenal doses in LAPC cases, albeit with slight increasing liver and spinal cord doses and increasing MU and the beam delivery time. Further evaluation is needed to know how the dose differences would affect the clinical outcomes in chemoradiotherapy for LAPC