Toshiaki Sasaki

Bayesian delineation framework of clinical target volumes for prostate cancer radiotherapy using an anatomical-features-based machine learning technique

Our aim was to develop a Bayesian delineation framework of clinical target volumes (CTVs) for prostate cancer radiotherapy using an anatomical-features-based machine learning (AF-ML) technique. Probabilistic atlases (PAs) of the pelvic bone and the CTV were generated from 43 training cases. Translation vectors, which could move the CTV PAs to CTV locations, were estimated using the AF-ML after a bone-based registration between the PAs and planning computed tomography (CT) images. An input vector derived from 11 AF points was fed to three AF-ML techniques (artificial neural network: ANN, random forest: RF, support vector machine: SVM). The AF points were selected from edge points and centroids of anatomical structures around prostate. Reference translation vectors between centroids of CTV PAs and CTVs were given to the AF-ML as teaching data. The CTV regions were extracted by thresholding posterior probabilities produced by using the Bayesian inference with the translated CTV PA and likelihoods of planning CT values. The framework was evaluated based on a leave-one-out test with CTV contours determined by radiation oncologists. Average location errors of CTV PAs along the anterior-posterior and superior-inferior directions without AF-ML were 5.7±4.6 mm and 5.5±4.3 mm, respectively, whereas the errors along the two directions with ANN, which showed the best performance, were 2.4±1.7 mm and 2.2±2.2 mm, respectively. The average Dice’s similarity coefficient between reference and estimated CTVs for 44 test cases were 0.81±0.062 with ANN. The framework using AF-ML could accurately estimate CTVs of prostate cancer radiotherapy.

Evaluation of Rotator Cuff Strength in Healthy Volunteers

【目的】健常人において，各種の肩関節筋力テスト結果を検討して，その特徴を示すことである．次に，利き手―非利き手間に筋力差があるかどうかを確認することである．【対象及び方法】肩の愁訴のないボランティア30例（男性が15例，女性が15例）を対象とした．平均年齢は36歳であった．両肩の筋力測定は徒手筋力測定器（MICROFET）を用いて測定した．各テストを3回計測して，平均値をもとめた．臨床筋力テストは外転筋力テストが40度テスト，Jobeテスト，90度テストの3種類とした．外旋筋力テストはISPテストとER IIテストの2種類，内旋筋力テストはBelly pressテストとSSCテストの2種類とした．【結果】外転筋力を100とすると外旋筋力は115，内旋筋力は160となった．利き手―非利き手間の筋力差は内外旋筋力テストではなく，外転筋力テストの中でJobeテストと90°テストにて有意差を認めた．女性の筋力は男性の60～70％であった．