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Abstrak :
ABSTRAK
Latar Belakang: Opasitas total hemitoraks kanan atas disebabkan dapat disebabkan oleh pneumonia, atelektasis dan massa. Ketiga etiologi tersebut sering ditemukan pada kondisi emergensi di mana ketiganya memiliki penanganan berbeda-beda, yaitu berupa antibiotik pada kasus pneumonia bronkoskopi emergensi pada kasus atelektasis, dan penataksanaan CT Scan toraks pada kasus massa paru. Penegakan diagnosis penyebab opasitas hemitoraks kanan atas tersebut dapat dilakukan melalui pemeriksaan CT Scan toraks dengan spesifisitas tinggi. Pemeriksaan radiografi toraks yang merupakan modalitas pencitraan pertama juga dapat membantu membedakan ketiga diagnosis ini dengan menilai tanda-tanda perubahan volume rongga toraks, salah satunya adalah jarak sela iga. Meskipun demikian, perubahan jarak sela iga ini masih bersifat kualitatif dan belum ditemukan penelitian mengenai titik potong yang dapat digunakan untuk menentukan penyebab opasitas total hemitoraks kanan atas. Tujuan : Meningkatkan nilai diagnostik radiografi toraks sebagai modalitas pemeriksaan awal pada kasus opasitas total hemitoraks kanan atas sehingga diagnosis dan tatalaksana yang diberikan semakin cepat dan akurat. Metode: Menggunakan desain korelatif dan komparatif studi potong lintang dengan data sekunder, sampel minimal 48 pasien. Analisis data berupa pengukuran korelasi rasio sela iga antara hemitoraks kanan dibanding kiri pada radiografi toraks dan CT Scan, penentuan titik potong menggunakan metode receiver operating curve (ROC) , serta penentuan tingkat sensitivitas dan spesifitasnya. Hasil: Perhitungan rasio sela iga pada radiografi toraks pada posisi AP maupun PA memiliki korelasi dengan CT Scan toraks dengan korelasi yang lebih kuat ditemukan antara radiografi toraks posisi AP dan CT Scan toraks. Terdapat perbedaan yang signifikan antara rasio sela iga midposterior kedua dan ketiga di antara kelompok atelektasis dengan pneumonia dan kelompok atelektasis dengan massa. Tidak terdapat perbedaan rasio sela iga antara kelompok pneumonia dan massa (kelompok non-atelektasis). Penggunaan titik potong sebesar 0,9 pada sela iga dua dapat membedakan kelompok atelektasis dan non-atelektasis dengan sensitivitas sebesar 77,8% dan spesifisitas sebesar 73,7%. Apabila titik potong 0,9 tersebut digunakan pada sela iga dua dan tiga, maka kelompok atelektasis dan non-atelektasis dapat dibedakan dengan sensitivitas sebesar 52,63% dan spesifisitas sebesar 93,75%. Kesimpulan : Pengukuran rasio sela iga pada radiografi toraks dapat digunakan untuk membedakan opasitas total hemitoraks kanan atas yang disebabkan oleh atelektasis dan non-atelektasis. Dengan membedakan kelompok atelektasis atau non atelektasis, maka pasien dapat lebih cepat untuk dilakukan tindakan yang invasif berupa bronkoskopi emergensi atau menjalani penanganan yang noninvasif seperti antibiotik pada konsolidasi pneumonia ataupun pemeriksaan lebih lanjut pada kasus massa.

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ABSTRACT
Background: Right upper hemithorax total opacities can be caused by pneumonia, ateletasis, and mass. These etiologies have some distinct treatments such as antibiotic for pneumonia, emergency bronchoscopy for ateletasis, and lung CT Scan for mass. Differentiation between these three causes can be made by chest CT Scan with high spesificity . Chest radiography which act as the first line modality can also help in differentiating between these etiologies by looking for the sign of hemithorax volume changes such as intercostal space. However, intercostal space changes is still measured qualitatively and there still no research about intercostal space cut-off for differentiating the caused of right upper hemithorax total opacities Purpose : Increasing diagnostic value of chest radiography which is the first line imaging in right upper hemithorax total opacities, to provide a better and faster treatment. Methods: This study is a corellative and comparative cross sectional study with secondary data and 48 minimal subject. The data were analysed by measuring the ratio between right and left intercostal spaces in chest radiography and CT Scan, determining the cut-off using receiver operating curve (ROC), and also determining the sensitivity and specificity. Result: The intercostal space ratio in AP and PA positions of chest radiography has correlation with the intercostal space ratio in chest CT Scan, which is found higher in AP position. There is a significant difference between intercostal ratio in second and third intercostal at midposterior position between atelectasis and pneumonia group, and also between atelectasis and mass group. There is no significant difference between intercostal ratio in pneuimonia and mass group. By using 0,9 as a cut off in the second midposterior intercostal, atelectasis and non atelectasis group can be differentiate with sensitivity and specificity 77,8% and 73,7% respectively. By using 0,9 as a cut of in both of second and third midposterior intercostal, atelectasis and non atelectasis group can be differentiate with sensitivity and specificity 52,63% and 93,75% respectively Conclusion: Intercostal space ratio measurement in chest radiography can be used to differentiate right upper hemithorax total opacities, especially by atelectasis and non atelectasis. By defferentiating between atelectasis and non atelectasis groups, the patient can get a faster invasive treatment such as emergency bronchoscopy or proceed to non invasive therapy such as antibiotic in pneumonia or chest CT Scan in mass.

Abstrak :
At this time there is an increasing demand for an accurate pre operative staging in non small cell lung cancer. Chest Computed Tomography (CT) is one of the imaging modality of choice used for this purpose. This study evaluated the accuracy of the chest CT to determine the status of tumor and nodules in non small cell lung cancer. During the years 1998 and 1999 a descriptive prospective study of 32 patients undergoing a contact enhanced chest CT examination for non small cell lung cancer, stage I-IIIA, was conducted. Lobectomy, lu\ymph nodes dissection and postoperatice histo-pathological examination were done. CT findings were as followas a sensitivity of 100% , a specificity of 25% and an accuracy of 60% in the detection of the nodule stage were found. In 17 patients with adeno-carcinoma, the sensitivity, the specificity and the accuracy were 86.6%, 100% dan 88.2% respectively. The diagnosis of all patients was conformed histo-pathologically. Six patients with T2 dan 26 patients with T3 were detected by chest CT; the accuracy of the tumor status was 93.7% confirmed by surgical and histo-pathological examinations. It was concluded that th CT played an important role in determining the clinical stage of non small cell lung cancer. The specificity and accuracy were higher in adeno-carcinoma as compare with squamous cell carcinoma in detecting the nodal status.

Abstrak :
Kanker paru merupakan kanker yang paling banyak ditemukan dan paling mematikan di dunia. Penentuan stadium kanker paru umumnya dilakukan oleh dokter radiologi dengan melihat pembesaran kelenjar getah bening (KGB) mediastinal. KGB mediastinal cukup sulit dideteksi secara visual dikarenakan memiliki kontras yang rendah  terhadap jaringan di sekitarnya, ukuran dan bentuknya yang bervariasi, serta tersebar di berbagai lokasi. Oleh karena itu, akhir – akhir ini dikembangkan sistem computer-aided detection (CADe) sebagai alat bantu bagi dokter radiologi untuk mendeteksi KGB mediastinal secara otomatis. Metode terbaik saat ini dalam sistem CADe KGB mediastinal tersebut menggunakan 2D convolutional neural network (CNN) yang diterapkan dari 3 sudut pandang (axial, coronal, sagittal). Namun, sifat 3D dari KGB mediastinal dihipotesakan akan lebih terwakili jika menggunakan 3D CNN. Oleh karena itu, dalam penelitian ini digunakan 3D CNN yang kemudian diubah menjadi 3D fully convolutional network (FCN)  untuk mendeteksi kandidat KGB mediastinal di dalam suatu tumpukkan citra CT. Kandidat KGB mediastinal tersebut kemudian dianalisa untuk mengurangi false positive (FP) menggunakan 3 metode, yaitu perhitungan mean HU, deteksi kontur menyerupai lingkaran, dan klasifikasi menggunakan 3D CNN. Performa terbaik dari sistem CADe KGB mediastinal ini diperoleh ketika menggunakan 3D CNN dalam tahap pengurangan FP dengan sensitivitas 77% dan 12 FP/pasien.

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Lung cancer is the most common and the deadliest cancer in the world. Lung cancer staging usually was done by radiologist by detecting mediastinal lymph node (LN) enlargement. Mediastinal LN is difficult to be detected visually due to its low contrast to the surrounding tissues, various size and shape, and sparse location. Therefore, computer-aided detection (CADe) system has been developed as a tool for radiologist to detect medistinal LN automatically. The state of the art mediastinal LN CADe system used 2D convolutional neural network (CNN) from 3 planar views (axial, coronal, sagittal). However, the 3D features of mediastinal LN are hypothesized to be more reprenseted if 3D CNN is used. Therefore, in this experiment we used 3D CNN which is converted to 3D fully convolutional network (FCN) to detect mediastinal LN candidate in a stack of CT images. Then, the mediastinal LN candidates were analyzed using 3 methods to reduce the false positive (FP), which are the calculation of the mean HU, the blob detection, and the classification using 3D CNN. The best performance of this CADe system was achieved when the 3D CNN was used in the FP reduction stage which has 77% of sensitivity and 12 FP/ patient.