1.Efficacy and Safety of Low-Dose-Rate Endorectal Brachytherapy as a Boost to Neoadjuvant Chemoradiation in the Treatment of Locally Advanced Distal Rectal Cancer: A Phase-II Clinical Trial.
Shapour OMIDVARI ; Shadi ZOHOURINIA ; Mansour ANSARI ; Leila GHAHRAMANI ; Mohammad ZARE-BANDAMIRI ; Ahmad MOSALAEI ; Niloofar AHMADLOO ; Saeedeh POURAHMAD ; Hamid NASROLAHI ; Sayed Hasan HAMEDI ; Mohammad MOHAMMADIANPANAH
Annals of Coloproctology 2015;31(4):123-130
PURPOSE: Despite advances in rectal cancer treatment over the last decade, local control and risk of late side effects due to external beam radiation therapy (EBRT) remain as concerns. The present study aimed to investigate the efficacy and the safety of low-dose-rate endorectal brachytherapy (LDRBT) as a boost to neoadjuvant chemoradiation for use in treating locally advanced distal rectal adenocarcinomas. METHODS: This phase-II clinical trial included 34 patients (as the study arm) with newly diagnosed, locally advanced (clinical T3-T4 and/or N1/N2, M0) lower rectal cancer. For comparative analysis, 102 matched patients (as the historical control arm) with rectal cancer were also selected. All the patients were treated with LDRBT (15 Gy in 3 fractions) and concurrent chemoradiation (45-50.4 Gy). Concurrent chemotherapy consisted of oxaliplatin 130 mg/m2 intravenously on day 1 plus oral capecitabine 825 mg/m2 twice daily during LDRBT and EBRT. RESULTS: The study results revealed a significant differences between the study arm and the control arm in terms in the pathologic tumor size (2.1 cm vs. 3.6 cm, P = 0.001), the pathologic tumor stage (35% T3-4 vs. 65% T3-4, P = 0.003), and the pathologic complete response (29.4% vs. 11.7%, P < 0.028). Moreover, a significantly higher dose of EBRT (P = 0.041) was found in the control arm, and a longer time to surgery was observed in the study arm (P < 0.001). The higher rate of treatment-related toxicities, such as mild proctitis and anemia, in the study arm was tolerable and easily manageable. CONCLUSION: A boost of LDRBT can optimize the pathologic complete response, with acceptable toxicities, in patients with distal rectal cancer.
Adenocarcinoma
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Anemia
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Arm
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Brachytherapy*
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Drug Therapy
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Humans
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Neoadjuvant Therapy
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Proctitis
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Rectal Neoplasms*
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Capecitabine
2.Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.
Shahram PAYDAR ; Elahe PARVA ; Zahra GHAHRAMANI ; Saeedeh POURAHMAD ; Leila SHAYAN ; Vahid MOHAMMADKARIMI ; Golnar SABETIAN
Chinese Journal of Traumatology 2021;24(1):48-52
PURPOSE:
The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.
METHODS:
The study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014-2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients' prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria.
RESULTS:
Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.
CONCLUSION
Data mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.