Encapsulating fibrosis following neoadjuvant chemotherapy is correlated with outcomes in patients with pancreatic cancer.
Yoko MatsudaYosuke InoueMakiko HiratsukaShoji KawakatsuTomio AraiKiyoshi MatsuedaAkio SaiuraYutaka TakazawaPublished in: PloS one (2019)
Pathological assessments of the treatment effect are critical for predicting patient outcomes after surgery. This study included 82 localized pancreatic cancer, 40 of whom were treated with neoadjuvant therapy (NAT) using four courses of gemcitabine plus nab-paclitaxel (GnP) followed by pancreatectomy (GnP group). The remaining 42 patients were treated with upfront pancreatectomy (UP) followed by adjuvant chemotherapy (UP group). We reviewed clinicopathological data of these patients to assess differences between the GnP and UP groups and further evaluate the prognostic impact of residual tumors after GnP treatment. Adjuvant treatment (S1, GnP or gemcitabine) was administered for 36 patients in the GnP group and 33 patients in the UP group. Compared to the UP group, the GnP group showed lower serum CA19-9 levels, microscopic tumor volume, and tumor-stroma ratio and decreased number of lymph node metastasis and vascular invasion. Higher incidence of encapsulating fibrosis was observed in the GnP group than in the UP group. Relative to the UP group (69%), a higher R0 rate was observed in the GnP group (85%). As for prognosis, encapsulating fibrosis was correlated with the overall survival of patients in the GnP group. However, overall survival did not show any correlation with other clinicopathological factors, including tumor reduction ratio (determined by computed tomography) and tumor regression grade (determined following criteria of Evans' grading system or those of the College of American Pathologists). In conclusion, the present study revealed that GnP-induced encapsulating fibrosis could predict patients' outcome. Nevertheless, large cohort studies are warranted to further evaluate the prognostic value of fibrosis, possibly with the help of imaging and biomarkers.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- computed tomography
- type diabetes
- prognostic factors
- mass spectrometry
- skeletal muscle
- positron emission tomography
- stem cells
- patient reported outcomes
- locally advanced
- radiation therapy
- metabolic syndrome
- machine learning
- mesenchymal stem cells
- electronic health record
- oxidative stress
- artificial intelligence
- insulin resistance
- big data
- combination therapy
- smoking cessation
- patient reported
- cell migration
- high glucose