Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies

Haeno, Hiroshi; Gönen, Mithat; Davis, Meghan B.; Herman, Joseph M.; Iacobuzio‐Donahue, Christine A.; Michor, Franziska

doi:10.1016/j.cell.2011.11.060

Cited by 374 publications

(354 citation statements)

References 46 publications

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“…The authors studied tumor size and growth kinetics and found that a consideration of both parameters offers an opportunity to optimize the timing of therapy. This results of this study using a complex mathematical model supports the notion that most patients harbor metastatic disease at the time of diagnosis [13]. This may not be a surprise to clinicians who treat this disease on a regular basis, but the fact that the outcome of the model predicts what we know clinically is extremely promising for future development of more sophisticated computational models.…”

Section: The Diffusion-proliferation Modelsupporting

confidence: 76%

“…The investigators review findings which support the notion that metastasis is a late event in the clonal evolution of pancreatic cancer [13]. By using data from patients who died of pancreatic cancer, with tumors measured at multiple time points, the study shows that an exponential growth model more accurately fits (median R 2 =0.63) the clinical growth pattern than a linear growth model.…”

Section: The Diffusion-proliferation Modelmentioning

confidence: 95%

“…These same investigators then applied their descriptive tumor model to patients with adenocarcinoma of the pancreas [13]. In this study, they used data from 228 patients with adenocarcinoma of the pancreas and evaluated growth kinetics.…”

Section: The Diffusion-proliferation Modelmentioning

confidence: 99%

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Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Lefor¹

2017

Dig Sys

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The mathematical modeling of tumors has potential to contribute to individualized therapy for a wide range of malignancies. There have been many approaches to this complex subject over the last 50 years or more. The current study is an attempt to adapt a model, originally developed for neuro-oncology, to the understanding of hepatic metastases from pancreatic carcinoma. The Diffusion-Proliferation (DP) model uses the sum of a diffusion term and a proliferation term to calculate changes in tumor cell concentration over time. The model was adapted directly from previous investigators who have used it to study brain tumors. The model is based on two parameters, a diffusion parameter (D i ) and a proliferation index (p). LabVIEW is a widely used software package, which enables simulation of physical systems, especially in engineering applications, and has been applied to the diagnosis and treatment of malignancies. This is the first study to use LabVIEW for the simulation of tumor growth. The simulation allows direct entry of the two parameters, D and p, with the resulting tumor growth curve appearing as a graph. The user has a rapid graphical understanding of the impact of the two parameters, D and p, on tumor growth. The next step in the development of this model will be to use patient-specific data to understand tumor growth and calculate survival based on the parameters D and p.

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Section: The Diffusion-proliferation Modelsupporting

confidence: 76%

Section: The Diffusion-proliferation Modelmentioning

confidence: 95%

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Simulating the growth of hepatic metastases from carcinoma of the pancreas using LabVIEW

Lefor¹

2017

Dig Sys

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“…This model revealed that all cases are expected to harbor metastasis‐enabled cells in the primary tumor at the time of diagnosis. Interestingly, a case with the primary tumor of 10 mm has a probability of 28% of harboring metastases at diagnosis; as the primary tumor size increases to 20 mm and 30 mm, the risk of harboring metastases increases to 73 and 94%, respectively 13. These results suggest that PC of ≤10 mm with a low potential of metastasis and a favorable prognosis may be defined as ‘early PC’.…”

Section: Ts1a As ‘Early Pancreatic Cancer’mentioning

confidence: 94%

Early diagnosis of pancreatic cancer: Current trends and concerns

Hanada

Amano

Abe

2017

Annals of Gastroent Surgery

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Early detection of pancreatic cancer (PC) is essential for a better prognosis. Some recent studies have demonstrated that a slight dilatation of the main pancreatic duct (MPD) and small cystic lesions were detected initially in most cases diagnosed at an early stage. Detecting these abnormal findings in cases with high risk factors through an effective screening system including image diagnosis, some biological markers, or familial cancer registrations should contribute to early diagnosis of PC. It has been reported that endoscopic ultrasonography (EUS) is essential for detecting tumors <10 mm with a favorable prognosis. Additionally, EUS‐guided fine‐needle aspiration biopsy is useful for confirming final histological diagnosis. For the diagnosis of stage 0 PC, local irregular stenosis of MPD should be an important initial abnormal sign detected by EUS or magnetic resonance cholangiopancreatography. Cytodiagnosis multiple times using pancreatic juice obtained by endoscopic nasopancreatic drainage should be essential for the final diagnosis. Recently, activities of regional networks between specialist doctors in medical centers and general practitioners for early diagnosis of PC have been reported in Japan. In the future, these activities may play an important role in the early diagnosis of PC.

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“…2 Ultimately, these patients also succumb to metastatic or recurrent PDA, suggesting that microscopic dissemination is an early hallmark of the disease. 3 Against this relentlessly challenging clinical backdrop, substantial progress has been made toward defining the genetic alterations that contribute to pancreatic cancer initiation and progression. Oncogenic Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations are found in approximately 95% of pancreatic cancer patients and function as an initiating event that is further compounded by additional mutations or loss of tumor suppressor genes such as tumor protein p53 (TP53) and SMAD family member 4 (SMAD4).…”

mentioning

confidence: 99%