The risk of malignancy index (RMI) in diagnosis of ovarian malignancy: A case report

Background: Ovarian cancer is among the deadliest gynecological cancers, with nearly 70% of cases identified at an advanced stage. It is the fourth most common cause of cancer-related deaths in women. While early detection significantly improves ovarian cancer outcomes, the disease often presents no symptoms in its early stages, resulting in delayed diagnosis and worsened prognosis. Achieving an accurate initial diagnosis in women with ovarian cancer is essential for ensuring early, correct treatment and reducing the risk of overtreatment. Clinically, various methods are available to assess ovarian malignancy risk, including the Risk of Malignancy Index (RMI). Purpose: To demonstrate the use of the RMI score to differentiate between benign and malignant ovarian tumors. Method: The case of Mrs. LKS, a 34-year-old woman who had experienced abdominal distension and related symptoms for one year, was analyzed in detail. The patient underwent multiple diagnostic evaluations, including a physical exam, ultrasound, abdominal CT scan, and histopathological analysis. The RMI, a well-established malignancy risk assessment tool, was employed. The initial evaluation of ovarian malignancy risk primarily considered ultrasound results, menopausal status, and serum CA-125 levels, with an RMI score above 200 indicating a high risk of ovarian cancer. Surgical procedures performed included a total hysterectomy, omentectomy, appendectomy, and ascitic fluid drainage. Results: The patient’s main issue was a gradually worsening abdominal distension over the past year, accompanied by lower abdominal pain, nausea, and significant weight loss. Physical examination and imaging revealed a heterogeneous cystic mass in the abdomen, suggesting a malignant ovarian cyst, along with the presence of ascites. The RMI score exceeded 200, indicating a high risk of ovarian malignancy. The patient underwent a laparotomy, which included a total hysterectomy, omentectomy, and appendectomy, leading to a postoperative diagnosis of stage III B ovarian cancer. Despite the intervention, the prognosis remained poor (dubia ad malam) due to the advanced stage at diagnosis. Conclusion: The RMI calculation serves as a valuable and reliable method for guiding diagnostic, management, and treatment decisions for patients with benign and malignant ovarian masses.

Authors

Institutions

Publication Details

Journal
Malahayati International Journal of Nursing and Health Science
Published
2025-03-23
DOI
https://doi.org/10.33024/minh.v8i1.664
Primary Topic
Ovarian cancer diagnosis and treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The risk of malignancy index (RMI) in diagnosis of ovarian malignancy: A case report

Renardy Reza Razali, Daniel Anugrahta Sembiring
Malahayati International Journal of Nursing and Health Science
Ovarian cancer diagnosis and treatment
article

The risk of malignancy index (RMI) in diagnosis of ovarian malignancy: A case report

Renardy Reza Razali, Daniel Anugrahta Sembiring
article en

Abstract

Background: Ovarian cancer is among the deadliest gynecological cancers, with nearly 70% of cases identified at an advanced stage. It is the fourth most common cause of cancer-related deaths in women. While early detection significantly improves ovarian cancer outcomes, the disease often presents no symptoms in its early stages, resulting in delayed diagnosis and worsened prognosis. Achieving an accurate initial diagnosis in women with ovarian cancer is essential for ensuring early, correct treatment and reducing the risk of overtreatment. Clinically, various methods are available to assess ovarian malignancy risk, including the Risk of Malignancy Index (RMI). Purpose: To demonstrate the use of the RMI score to differentiate between benign and malignant ovarian tumors. Method: The case of Mrs. LKS, a 34-year-old woman who had experienced abdominal distension and related symptoms for one year, was analyzed in detail. The patient underwent multiple diagnostic evaluations, including a physical exam, ultrasound, abdominal CT scan, and histopathological analysis. The RMI, a well-established malignancy risk assessment tool, was employed. The initial evaluation of ovarian malignancy risk primarily considered ultrasound results, menopausal status, and serum CA-125 levels, with an RMI score above 200 indicating a high risk of ovarian cancer. Surgical procedures performed included a total hysterectomy, omentectomy, appendectomy, and ascitic fluid drainage. Results: The patient’s main issue was a gradually worsening abdominal distension over the past year, accompanied by lower abdominal pain, nausea, and significant weight loss. Physical examination and imaging revealed a heterogeneous cystic mass in the abdomen, suggesting a malignant ovarian cyst, along with the presence of ascites. The RMI score exceeded 200, indicating a high risk of ovarian malignancy. The patient underwent a laparotomy, which included a total hysterectomy, omentectomy, and appendectomy, leading to a postoperative diagnosis of stage III B ovarian cancer. Despite the intervention, the prognosis remained poor (dubia ad malam) due to the advanced stage at diagnosis. Conclusion: The RMI calculation serves as a valuable and reliable method for guiding diagnostic, management, and treatment decisions for patients with benign and malignant ovarian masses.

Malahayati International Journal of Nursing and Health ScienceVol. 8(1)
Riau University (ID)
Openalex Percentile: Top 90%
Ovarian cancer diagnosis and treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.