Krukenberg Tumors: Update on Imaging and Clinical Features

Zulfiqar, Maria; Koen, J.A.; Nougaret, Stéphanie; Bolan, Candice W.; VanBuren, Wendaline M.; McGettigan, Melissa; Menias, Christine O.

doi:10.2214/ajr.19.22184

Cited by 42 publications

(51 citation statements)

References 48 publications

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“…The "seed and soil" theory has been established as the basic theory 17 . Besides, about 50% of the metastatic tumors of the ovary are Krukenberg tumors, most of them came from SRCC 18 . Gastric SRCC is the most common primary tumor, followed by colorectal cancer.…”

Section: Resultsmentioning

confidence: 99%

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Deng

et al. 2022

Sci Rep

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Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

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Section: Resultsmentioning

confidence: 99%

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Deng

et al. 2022

Sci Rep

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“…In the present case, the ultrasound findings showed a smooth surface tumor with cystic components typical of a Krukenberg tumor. The specificity of the ultrasound findings is reportedly 96.5% [6], which was considered a characteristic finding of the Krukenberg tumor in this case [6].…”

Section: Discussionmentioning

confidence: 49%

“…Third, few cases of Krukenberg tumor causing stalk torsion have been reported [5]. Fourth, in the typical CT findings of the Krukenberg tumor, the cystic and solid components are often mixed in the ovarian tumor [6], but in this case, only the cystic component was present.…”

Section: Discussionmentioning

confidence: 82%

“…It has been reported that it is difficult to differentiate mucinous ovarian cancer from metastatic ovarian cancer pathologically [6]. However, the ultrasound findings of Krukenberg tumors are characterized by a smooth tumor surface with cystic components inside, which is different from primary ovarian cancer with solid components and papillary projections inside the tumor [1].…”

Section: Discussionmentioning

confidence: 99%

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A Case of Ovarian Metastasis of Pancreatic Cancer Causing Ovarian Torsion

Tanabe¹,

Sugino²,

Ichida³

et al. 2022

Cureus

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A 55-year-old female who had been diagnosed with stage 4 pancreatic cancer visited the emergency department of our hospital owing to sudden lower abdominal pain and was found to have an ovarian tumor. An urgent laparoscopic resection of the right adnexa was performed with a diagnosis of ovarian tumor stalk torsion. Postoperative pathological diagnosis revealed ovarian metastasis of pancreatic cancer. Our case report demonstrates that an ovarian tumor on one side in a patient with advanced pancreatic cancer may represent ovarian metastasis of the primary tumor.

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“…Besides, about 50% of the metastatic tumors of the ovary are Krukenberg tumors, most of them came from SRCC [18]. Gastric SRCC is the most common primary tumor, followed by colorectal cancer.…”

Section: Different Forms Of Metastatic Lesionsmentioning

confidence: 99%

Quantifying the Cell Morphology and Predicting Biological Behavior of Signet Ring Cell Carcinoma using Deep Learning

Deng

Li³

et al. 2021

Preprint

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Signet ring cell carcinoma(SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning(DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images(WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

show abstract

Krukenberg Tumors: Update on Imaging and Clinical Features

Cited by 42 publications

References 48 publications

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

A Case of Ovarian Metastasis of Pancreatic Cancer Causing Ovarian Torsion

Quantifying the Cell Morphology and Predicting Biological Behavior of Signet Ring Cell Carcinoma using Deep Learning

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