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Triple Diagnostic Accuracy on Early Stage Breast Cancer at dr. Cipto Mangunkusumo and Persahabatan General Hospital Kartini, Diani; Megatia, Ika; Darmiati, Sawitri; Rustamadji, Primariadewi; Budiningsih, Setyawati
The New Ropanasuri Journal of Surgery
Publisher : UI Scholars Hub

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Abstract

Introduction. Breast cancer is the most common cancer in Indonesia with incidence rate 40.3 per 100.000 women and mortality rate 16.6 per 100.000women. On early stage, the decision for operative procedure (i.e. mastectomy) requires intraoperative frozen section to assess malignancy; which is mostly unavailable in secondary hospitals. The triple diagnostic (TD) test consists of physical examination, breast ultrasonography and fine needle aspiration biopsy is an accurate and simple preoperative diagnostic method that may solve the problem. The study aimed to find out conformance of the triple diagnostic to histopathology findings in those with breast lump where the malignancy was suspected. Method. A study of diagnostic accuracy conducted enrolling subjects with suspected malignant breast lump managed in dr Cipto Mangunkusumo General Hospital (RSCM) and Persahabatan Hospital (RSP) in period of February 2016 to August 2017 who met the criteria: those underwent preoperative triple diagnostic, intraoperative frozen section and histopathology examination. The conformance of TD and frozen section were compared to histopathology findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were the focuses of the study. Results. There were 33 subjects enrolled (prevalence of 4.3%), mean age of 49.6 years ± 10.9, were above 40 years (78.8%). Tumor size of 2–5 cm found in 63.6% subjects, and the most histopathology finding was invasive carcinoma (84.8%). Frozen section showed sensitivity of 96.8%, specificity of 100%, PPV of 100%, NPV of 66.7% and accuracy of 97.0%. TD showed sensitivity of 77.4%, specificity of 100%, PPV of 100%), NPV of 22.2% and accuracy of 78.8% (p = 0.016). Conclusion. Triple diagnostic reaches up to 78% accuracy on early stage breast cancer may be used secondary hospital in Indonesia whenever frozen section is unavailable.
Holoprosensefali Alobar -, Biddulth; Darmiati, Sawitri; Handriyastuti, RM Setyo
Cermin Dunia Kedokteran Vol 44, No 1 (2017): Nutrisi
Publisher : PT. Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (459.657 KB) | DOI: 10.55175/cdk.v44i1.809

Abstract

Holoprosensefali adalah anomali struktur parenkim otak berupa kegagalan pemisahan komplit otak depan, akibat kegagalan pemisahan prosensefalon dalam tahap perkembangan otak. Holoprosensefali ditemukan pada 1 dari 250 fetus, dan 1 dalam 16.000 kelahiran hidup. Kebanyakan fetus akan meninggal, dan yang lahir hidup umumnya tidak lebih dari 1 tahun. Laporan kasus: Seorang anak perempuan, berusia 1 tahun datang dengan keadaan gizi buruk sejak 2 bulan. Saat lahir pasien menderita bibir sumbing, yang dioperasi pada usia 7 bulan. Sejak dalam kandungan, pasien telah didiagnosis memiliki kelainan kongenital celah langit-langit dan bibir. Hasil pemeriksaan MRI kepala sesuai gambaran holoprosensefali alobar.Holoprocencepahly is congenital brain anomaly caused by complete failure of the forebrain cleavage. It is found 1 in 250 fetus and 1 in 16.000 live birth. Most died within 1 year of life. Case report : A one-year old female child came with marasmus since 2 month. She had labiopalatoschizis, which was operated at 7 months. Brain MR findings consistent with alobar holoprosencephaly.
Impact of Artificial Intelligence on Mammography Interpretation by Breast Radiologists, Non-Breast Radiologists, and Senior Residents Darmiati, Sawitri; Afifi, Rahmi; Billy, Christy Amanda; Panigoro, Sonar Soni; Kartini, Diani; Prihartono, Joedo
Indonesian Journal of Cancer Vol 17, No 4 (2023): December
Publisher : http://dharmais.co.id/

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33371/ijoc.v17i4.1100

Abstract

Background: Artificial intelligence (AI) is recognized to have tremendous potential to revolutionize breast cancer management through mammography. However, the extent of its impact on radiologists with different levels of experience remains largely unexplored. Therefore, this study aimed to comprehensively show how AI could assist radiologists of varying expertise including breast and non-breast radiologists, as well as senior residents, in performing mammogram interpretation.Methods: This retrospective study analyzed eligible mammograms from Cipto Mangunkusumo Hospital between January 2017 and March 2021. Mammographic readings were conducted independently by two breast radiologists, two from other subspecialties, and three senior residents, all blinded to clinical information. AI standalone performance, as well as radiologists with and without AI assistance, was measured. Results: The results showed that a total of 886 eligible mammograms were analyzed. AI standalone performance, assessed using ROC curve analysis, yielded an AUC of 0.946 (95% CI, 0.925–0.967) with sensitivity and specificity of 90.1% and 93.6%, respectively. AI assistance significantly improved the sensitivity and specificity of all radiologists, regardless of experience level, with a median increase of 19.4% (IQR, 10.4–33.5%) and 12.1% (IQR, 5.2–16.2%), respectively. Moreover, there was a trend toward a higher increase with AI assistance in dense compared to fatty breasts.Conclusions: AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue. The results were derived from a national referral tertiary hospital that generally received many breast cancer cases referred from other hospitals for further treatment. Therefore, further studies incorporating different levels of hospitals were needed.
Tumor apparent diffusion coefficient value and ratio in magnetic resonance imaging on cervical cancer Siregar, Trifonia Pingkan; Wanandi, Septelia Inawati; Darmiati, Sawitri; Kusuma, Fitriyadi; Sekarutami, Sri Mutya; Lisnawati; Prihartono, Joedo; Ilyas, Muhammad; Amalia, Ginva; Elfahmi, Khalida Ikhlasiya Tajdar Gefariena
Medical Journal of Indonesia Vol. 34 No. 2 (2025): June
Publisher : Faculty of Medicine Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13181/mji.oa.257715

Abstract

BACKGROUND Diffusion-weighted magnetic resonance imaging (DW-MRI) is a noninvasive, non-contrast sequence for cancer detection. Research involving DW-MRI in cervical cancer has revealed lower apparent diffusion coefficient (ADC) values. This study aimed to evaluate the difference in tumor ADC values and ADC ratios (tumor-to-urine and tumor-to-muscle) with respect to tumor staging (early versus late) and histopathology (squamous cell carcinoma versus adenocarcinoma). METHODS This retrospective study included 56 patients with cervical cancer, divided into early- and late-stage groups. DW-MRI was performed in all patients, and the tumor ADC value, ADC ratio between the tumor and urine (ADC ratiot−u), and ADC ratio between the tumor and gluteal muscle (ADC ratiot−m) were measured. Statistical methods were employed to assess the difference in the tumor ADC value, ADC ratiot−u, and ADC ratiot−m with respect to cervical cancer stages and histopathological findings. RESULTS The median tumor ADC value was lower in the early-stage group than in the late-stage cervical cancer (0.75 × 10−3 mm²/s versus 0.8 × 10−3 mm²/s, p = 0.022). However, no differences were observed in ADC ratiot−u and ADC ratiot−m concerning the tumor staging, nor in ADC value, ADC ratiot−u, and ADC ratiot−m concerning histopathological findings (p = 0.29, 0.67 and 0.35, respectively), with no significant differences in the ADC ratiot−u (p = 0.153) and ADC ratiot−m (p = 0.260). In receiver operating characteristic analysis, the tumor ADC value was 75.0% sensitive and 50.0% specific in predicting late-stage cervical cancer with a cut-off value of 0.750 × 10−3 mm2/s. CONCLUSIONS The median tumor ADC value in early-stage patients was significantly lower than in the late-stage patients, suggesting that tumor ADC value has valuable potential for characterizing cervical cancer staging.