18F-FDG PET/CT in the detection of asymptomatic malignant melanoma recurrence

2017 ◽  
Vol 56 (03) ◽  
pp. 83-89 ◽  
Author(s):  
Ismaheel Lawal ◽  
Thabo Lengana ◽  
Kehinde Ololade ◽  
Tebatso Boshomane ◽  
Florette Reyneke ◽  
...  

SummaryAim: To evaluate the diagnostic accuracy of FDG PET/CT in the detection of asymptomatic recurrence in patients with malignant melanoma who have had resection of their primary lesion. We also aimed to determine the pattern and factors predisposing to disease recurrence. Methods: Patients with malignant melanoma who have had surgical resection of their disease and without any clinical evidence of disease recurrence were followed- up with FDG PET/CT. The diagnostic accuracy of FDG PET/CT, pattern of recurrence and factors predictive of disease recurrence were determined. Results: A total of 144 patients were followed-up for a median period of 50.50 months. Asymptomatic recurrence was seen in 37 patients (25.7 %) with a median time to recurrence of 20 months. Lymph node was the commonest site of asymptomatic recurrence. Sex, tumour depth, histology type and presence of nodal metastasis were significant predictors of tumour recurrence. Age, race, site of primary lesion, type of lymph node resection were not significant predictors of disease recurrence. Race has a significant effect on the histological subtype of tumour (nodular maligna was more common in Caucasian while acral lentiginous was more prevalent in the Blacks) and the site of the primary lesion (lower limb in Blacks and trunk in Caucasians). Sensitivity, specificity and accuracy of FDG PET/CT for the detection of disease recurrence were 94.5 %, 87.6 % and 89.6 % respectively. Conclusion: FDG PET/CT is a suitable modality for early detection of asymptomatic recurrence of malignant melanoma. Asymptomatic recurrence most commonly occurs in lymph nodes. Sex, nodal metastasis and tumour pathologic features are predictors of recurrence.

2017 ◽  
Vol 56 (03) ◽  
pp. 73-81 ◽  
Author(s):  
Joseph Cohnen ◽  
Benedikt Gomez ◽  
Johannes Grüneisen ◽  
Lino M. Sawicki ◽  
Herbert Rübben ◽  
...  

Summary Aim: To evaluate the diagnostic accuracy of FDG PET/CT in the detection of asymptomatic recurrence in patients with malignant melanoma who have had resection of their primary lesion. We also aimed to determine the pattern and factors predisposing to disease recurrence. Methods: Patients with malignant melanoma who have had surgical resection of their disease and without any clinical evidence of disease recurrence were followed- up with FDG PET/CT. The diagnostic accuracy of FDG PET/CT, pattern of recurrence and factors predictive of disease recurrence were determined. Results: A total of 144 patients were followed-up for a median period of 50.50 months. Asymptomatic recurrence was seen in 37 patients (25.7 %) with a median time to recurrence of 20 months. Lymph node was the commonest site of asymptomatic recurrence. Sex, tumour depth, histology type and presence of nodal metastasis were significant predictors of tumour recurrence. Age, race, site of primary lesion, type of lymph node resection were not significant predictors of disease recurrence. Race has a significant effect on the histological subtype of tumour (nodular maligna was more common in Caucasian while acral lentiginous was more prevalent in the Blacks) and the site of the primary lesion (lower limb in Blacks and trunk in Caucasians). Sensitivity, specificity and accuracy of FDG PET/CT for the detection of disease recurrence were 94.5 %, 87.6 % and 89.6 % respectively. Conclusion: FDG PET/CT is a suitable modality for early detection of asymptomatic recurrence of malignant melanoma. Asymptomatic recurrence most commonly occurs in lymph nodes. Sex, nodal metastasis and tumour pathologic features are predictors of recurrence.


2013 ◽  
Vol 24 ◽  
pp. iv29
Author(s):  
Jin Soo Kim ◽  
Kyung Ha Lee ◽  
Jeho Jang ◽  
Chang Nam Kim ◽  
Won Jun Choi ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zongyao Li ◽  
Kazuhiro Kitajima ◽  
Kenji Hirata ◽  
Ren Togo ◽  
Junki Takenaka ◽  
...  

Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.


2015 ◽  
Vol 29 (10) ◽  
pp. 1938-1944 ◽  
Author(s):  
E. Gellén ◽  
O. Sántha ◽  
E. Janka ◽  
I. Juhász ◽  
Z. Péter ◽  
...  

2013 ◽  
Vol 18 (3) ◽  
pp. 969-978 ◽  
Author(s):  
Philipp Heusch ◽  
Christoph Sproll ◽  
Christian Buchbender ◽  
Elena Rieser ◽  
Jan Terjung ◽  
...  

2008 ◽  
Vol 18 (5) ◽  
pp. 346-352 ◽  
Author(s):  
Baljinder Singh ◽  
Samer Ezziddin ◽  
Holger Palmedo ◽  
Michael Reinhardt ◽  
Holger Strunk ◽  
...  

2020 ◽  
Author(s):  
Zongyao Li ◽  
Kazuhiro Kitajima ◽  
Kenji Hirata ◽  
Ren Togo ◽  
Junki Takenaka ◽  
...  

Abstract Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies.Materials and Methods: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy.Results: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged.Conclusions: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.


2009 ◽  
Vol 36 (6) ◽  
pp. 910-918 ◽  
Author(s):  
Patrick Veit-Haibach ◽  
Florian M. Vogt ◽  
Robert Jablonka ◽  
Hilmar Kuehl ◽  
Andreas Bockisch ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document