Impact of Dosimetric Parameters on Tumor Control in Stereotactic Radiotherapy for Pancreatic Cancer: A Prospective Study on 104 Patients Treated with Simultaneous Integrated Protection (SIP)
Marco Lorenzo Bonù, Jacopo Balduzzi, Gloria Pedersoli, Dario Moneghini, Marco Ramera, Nazario Portolani, Jacopo Andreuccetti, Luigi Grazioli, Barbara Frittoli, Sarah Molfino, Anna Maria Bozzola, Maria Teresa Cefaratti, Eneida Mataj, Giulia Volpi, Luigi Spiazzi, Federica Saiani

TL;DR
This study shows that using a specific radiotherapy technique called SIP can effectively treat pancreatic cancer with high local control and low toxicity.
Contribution
The study identifies dosimetric parameters impacting tumor control in pancreatic cancer treated with SIP, a novel planning strategy.
Findings
A mean dose to the PTV_SIP of at least 28 Gy is associated with better local control.
PTV_SIP volume greater than 4 cc and whole PTV greater than 69 cc are linked to worse outcomes.
SIP planning achieved excellent local control with low toxicity in pancreatic cancer patients.
Abstract
Treating pancreatic cancer with stereotactic radiotherapy, a precise, high-dose radiotherapy modality, is challenging. The pancreatic gland moves with respiration, and more importantly, the duodenum, stomach, small bowel, and colon are highly sensitive to radiation. As a consequence, treating lesions in contact with such organs is highly complex. Moreover, planning in pancreatic cancer radiotherapy is highly related to physician experience and skill, and limited data are available concerning the major dosimetric variables influencing tumor control. In our prospective, single-arm study, 104 patients were treated with 45 Gy in six fractions, with the simultaneous integrated protection technique to better manage the area of intersection between critical organs and tumor. Our study showed excellent local control with low toxicity. We also identified important dosimetric variables impacting…
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Taxonomy
TopicsPancreatic and Hepatic Oncology Research · Advanced Radiotherapy Techniques · Radiomics and Machine Learning in Medical Imaging
