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"Kanker prostat merupakan masalah kesehatan yang umum terjadi pada pria di seluruh dunia. Diagnosis dini dan akurat sangat penting untuk pengobatan yang efektif. Spektroskopi Raman (RμS) menawarkan teknik yang menjanjikan untuk menganalisis sampel jaringan dan berpotensi membedakan antara jaringan prostat jinak dan ganas. Studi ini menyelidiki kemanjuran algoritma Machine Learning dalam mengklasifikasikan kanker prostat menggunakan data RS dari spesimen biopsi. Data Spektrum Raman yang digunakan berasal dari penelitian di Kanada yang dikumpulkan dari tiga kelompok Cohort yakni Centre Hospitalier de l’Universit´e de Montr´eal (CHUM) sebagai Cohort training, serta University Health Network (UHN) dan Centre Hospitalier de l’Universite de Montreal (CHUQc-UL) sebagai Cohort testing. Spektrum ini, yang mewakili komposisi kimia jaringan (Raw Spectra), digunakan untuk training dan model evaluation Machine Learning.Untuk membantu menganalisis komposisi kimia data RμS yang akurat, diperlukan algoritma Machine Learning dalam mengklasifikasikan BPH dan PC. Dua algoritma yang digunakan adalah Support Vector Machine (SVM) dan Extreme Gradient Boosting (XGBoost). Data RμS dilakukan training dan testing menggunakan data yang berbeda yang menghasilkan nilai metrik klasifikasi dari dua algoritma yang dibandingkan dalam mengklasifikasikan sampel BPH atau PC. Ternyata, algoritma XGBoost memiliki kemampuan klasifikasi yang kurang unggul daripada SVM, hal ini dibuktikan dengan SVM memiliki rata-rata Akurasi sebesar 83,3%, Sensitivitas sebesar 96.,7%, Spesifisitas sebesar 46,4%, F1-Score sebesar 98,1%, dan ROC-AUC sebesar 87,7%. Sementara, XGBoost menunjukkan Akurasi sebesar 78%, Sensitivitas sebesar 80%, Spesifisitas sebesar 75%, F1-Score sebesar 78%, dan ROC-AUC sebesar 85%.Selain itu kedua algoritma juga bisa menentukan titik Feature Importance pada grafik Spektra Raman, yang ditunjukkan dengan beberapa fitur vibrasi molekul untuk BPH dan PC berdasarkan algoritma SVM dan XGBoost, yakni 720 cm−1, 828 cm−1, dan 931 cm−1 sebagai karakteristik jaringan BPH, dan 1.431 cm−1 dan 1.470 cm−1 sebagai jaringan PC.

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Prostate cancer is a common health problem in men worldwide. Early and accurate diagnosis is essential for effective treatment. Raman spectroscopy (RμS) offers a promising technique to analyze tissue samples and potentially differentiate between benign and malignant prostate tissue. This study investigated the efficacy of a Machine Learning algorithm in classifying prostate cancer using RS data from biopsy specimens. The Raman Spectrum data used were from a Canadian study collected from three cohort groups, namely the Centre Hospitalier de l’Universit´e de Montr´eal (CHUM) as the training Cohort, and the University Health Network (UHN) and Centre Hospitalier de l’Universit´e de Montreal (CHUQc-UL) as the testing Cohort. This spectrum, which represents the chemical composition of the tissue (Raw Spectra), is used for training and model evaluation Machine Learning.

To help analyze the chemical composition of accurate RμS data, a Machine Learning algorithm is needed to classify BPH and PC. The two algorithms used are Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). The RμS data is trained and tested using different data that produces classification metric values from the two algorithms that are compared in classifying BPH or PC samples. It turns out that the XGBoost algorithm has a classification capability that is less superior than SVM, this is evidenced by SVM having an average Accuracy of 83.3%, Sensitivity of 96.7%, Specificity of 46.4%, F1-Score of 98.1%, and ROC-AUC of 87.7%. Meanwhile, XGBoost showed Accuracy of 78%, Sensitivity of 80%, Specificity of 75%, F1-Score of 78%, and ROC-AUC of 85%. In addition, both algorithms can also determine the Feature Importance point on the Raman Spectra graph, which is indicated by several molecular vibration features for BPH and PC based on the SVM and XGBoost algorithms, namely 720 cm−1, 828 cm−1, and 931 cm−1 as characteristics of BPH tissue, and 1.431 cm−1 and 1.470 cm−1 as PC tissue."

"Pemphigus vulgaris is an autoimmune–mediated disease of skin and mucous membran leading to progressive blistering and chronic erosions. It often begins with blister formations which easily rupture. The characteristic feature is positive nikolsky sign which may or not be presented simultaneously. Infrequently, ocular involvement may be seen as conjunctivitis. Establishment of early definite diagnosis is critical and requires correlation of clinical and histopathological findings. Because of this conditon is a potentially life-threatening, the risk of complications and mortality rate increases if initial management is non comprehensive and inadequate. Treatment is directed at supression of autoimmune process, typically administration of corticosteroids. This article report a case in a 51 years old woman who had painful chronic oral ulcer and poor general health condition. Prior to the visit to Oral Medicine clinic, patient was treated by her general practitioners for several months, without either established diagnosis nor comprehensive and adequate management, so that she had no clinical improvement. Clinical examination at the first visit in OralMedicine clinic Cipto Mangunkusumo Hospital, revealed bula in the skin, conjunctivitis, easily bleed oral mucosae, widespread erosions and ulcerations of the lips, gingiva, tongue, and buccal mucosae. Biopsy of one of skin erosions demonstarting suprabasal intraepithelial acantholysis. Multi divisions in Cipto Mangunkusumo Hospital, such as Oral Medicine, Dermatology, Internal Medicine, Ophtalmology, ENT, were involved in treating this case. Patient received high dose methylprednisolone sistemically and prednisone topically for the lips. General remission achieved in several days. Thus, establishment of early definite diagnosis and adequate management are important in management of Pemphigus vulgaris."

"Prostat menjadi salah satu penyakit berbahaya yang mematikan bagi pria di dunia. Pengobatan menggunakan terapi radiasi menjadi salah satu pilihan utama pada kanker prostat. Metode 3DCRT dan IMRT digunakan dalam perencanaan terapi radiasi untuk kanker prostat dengan Linac sebagai modalitas penyinaran yang berenergi 6X dengan dosis per fraksi berjumlah 2,5 Gy dan fraksi yang digunakan berjumlah 30. Tujuan dari penelitian ini adalah untuk verifikasi distribusi dosis radioterapi antara teknik 3DCRT dengan teknik IMRT untuk terapi kanker prostat dengan simulasi Monte Carlo menggunakan EGSnrc. 3DCRT menggunakan 4 lapangan dan IMRT menggunakan 5 lapangan untuk terapi. Hasil penelitian memperlihatkan transpor dosis yang bergerak untuk masing-masing lapangan dan terdistribusi pada organ target dan menunjukkan distribusi dosis pada PTV dan OAR (rectum dan bladder). Passing rate gamma index yang diperoleh untuk 3DCRT dan IMRT masing-masing sebesar 72,31 % dan 71,34% dimana masih belum mencapai passing rate yang ideal baik pada 3DCRT maupun IMRT.

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Prostate is one of the most deadly diseases for men in the world. Treatment using radiation therapy is one of the main options for prostate cancer. The 3DCRT and IMRT methods are used in planning radiation therapy for prostate cancer with Linac as the radiation modality with 6X energy with a dose per fraction of 2.5 Gy and the fraction used is 30. The purpose of this study is to verify the radiotherapy dose distribution between the 3DCRT technique and the IMRT technique for the treatment of prostate cancer with Monte Carlo simulation using EGSnrc. 3DCRT uses 4 fields and IMRT uses 5 fields for treatment. The results showed that the dose transport moved for each field and was distributed to the target organ and showed the dose distribution on PTV and Organ at Risk (rectum and bladder). The passing rate gamma index obtained (72,31% for 3DCRT and 71,34% for IMRT) has not yet reached the ideal passing rate for both 3DCRT and IMRT."

"Latar Belakang: Pemeriksaan dan diagnosis kanker prostat (PCa) diperlukan untuk memberikan manajemen optimal pada tahap awal. Meskipun telah dibahas dalam banyak pedoman, implementasi pemeriksaan dan diagnosis PCa di Indonesia masih belum diketahui. Studi ini bertujuan untuk mengevaluasi pola pemeriksaan dan diagnosis PCa di antara urolog Indonesia serta kepatuhan mereka terhadap pedoman.Metode: Studi potong lintang ini dilakukan antara Februari dan Juli 2019. Responden adalah urolog Indonesia yang terdaftar sebagai anggota Perhimpunan Urologi Indonesia (IUA) dan telah berpraktik selama setidaknya enam bulan. Data dikumpulkan menggunakan kuesioner yang dibagikan dalam simposium urologi nasional dan secara elektronik melalui Google Form. Data disajikan secara deskriptif, dan semua data diproses menggunakan SPSS versi 23.Hasil: Dari 458 urolog, 195 (42,6%) memberikan respons lengkap. Sebagian besar responden, 181 (92,8%) urolog, menggunakan pedoman IUA. Di antara 103 (52,8%) responden yang melakukan pemeriksaan, hampir separuh (42,7%) setuju untuk memeriksa pasien yang berusia ≥ 50 tahun atau ≥ 45 tahun dengan riwayat keluarga PCa. Selain itu, 76,8% akan mengulang pemeriksaan setiap tahun, dan 35,6% akan menghentikannya ketika pasien berusia 70 tahun. Pemeriksaan rektal digital (DRE) sering dilakukan untuk pemeriksaan (74,5%), sementara tes antigen spesifik prostat (PSA) hanya dilakukan dalam 52,3% kasus. Tes PSA tersedia di 74,8% rumah sakit. Reseksi transuretral prostat (TURP) masih digunakan oleh 67,2% responden untuk diagnosis. Hanya 52,3% peserta yang menggunakan biopsi prostat transrektal untuk diagnosis, menggunakan anestesi (78,1%) selama prosedur, dan peningkatan kadar PSA (98%) sebagai indikasi. Namun, USG Transrektal (TRUS) hanya tersedia di 49% rumah sakit. Studi ini menemukan bahwa tingkat kepatuhan urolog Indonesia terhadap pedoman adalah 63,3% (9-100%).Kesimpulan: Pemeriksaan dan diagnosis PCa masih bervariasi di antara urolog Indonesia, yang mungkin disebabkan oleh ketersediaan modalitas diagnostik yang berbeda.

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Background: Prostate cancer (PCa) screening and diagnosis are mandatory to deliver optimal management in the early phase. Even though it has been discussed in many guidelines, the implementation of PCa screening and diagnosis in Indonesia remains unknown. This study aims to evaluate the pattern of PCa screening and diagnosis among Indonesian urologists and their adherence to guidelines.

Methods: This cross-sectional study was conducted between February and July 2019. Respondents were Indonesian urologists registered as members of the Indonesian Urological Association (IUA) and had already practiced for at least six months. Data were collected using questionnaires, which were distributed at a national urology symposium and electronically via Google Form. Data were presented descriptively, and all data were processed using SPSS version 23.

Result: Of 458 urologists, 195 (42.6%) gave full responses. Most of the respondents, 181 (92.8%) urologists, used the IUA guidelines. Among the 103 (52.8%) respondents who performed screening, nearly half (42.7%) agreed to screen patients aged ≥ 50 years or ≥ 45 years with a family history of PCa. Moreover, 76.8% would repeat screening annually, and 35.6% would stop when the patient's age reached 70 years old. Digital rectal examination (DRE) was frequently performed for screening (74.5%), while prostate-specific antigen (PSA) tests were only performed in 52.3% of cases. The PSA test was available in 74.8% of hospitals. Transurethral resection of the prostate (TURP) was still used by 67.2% of respondents for diagnosis. Only 52.3 % of participants used transrectal prostate biopsy for diagnosis, using anesthesia (78.1%) during the procedure, and increased PSA level (98%) as its indication. However, Transrectal Ultrasound (TRUS) was only available in 49% of hospitals. This study found that Indonesian urologist adherence level toward guidelines was 63.3% (9-100%).

Conclusion: PCa screening and diagnosis are still varied among Indonesian urologists, which might arise due to the different availability of diagnostic modalities."