Effect of increasing radiation dose on pathologic complete response in rectal cancer patients treated with neoadjuvant chemoradiation therapy

507 Background: Several studies show locally advanced rectal cancer patients with clinical complete response (cCR) have comparable oncologic outcomes to pathologic complete response (pCR) to neoadjuvant chemoradiation therapy (NCRT). Thus, total mesorectal excision (TME) could potentially be avoided. Our objective was to validate macroscopic features of cCR. Methods: 164 patients with stage II/III rectal cancer were previously enrolled in a phase II trial and treated by NCRT and TME. Tumor response in the surgical specimens was assessed according to AJCC guidelines. A pCR was defined as absence of viable tumor cells. Gross macroscopic features by the pathologist were tabulated and our cohort was applied to previously published cCR criteria. Results: 25.0% (n = 41) had pCR; 75.0% (n = 123) had non-pCR. Descriptors were condensed into 14 macroscopic features by combining terms and excluding those rarely mentioned. Several reports affirm that scarring signifies cCR, while others suggest that fibrosis, edema, ulceration and nonpalpable tumor to be consistent with cCR. In our study, scarring was found in 6.1% of patients, 16.7% of which had pCR. We found that hyperemia, scarring, flat, smooth, and tan-pink mucosa were significantly associated with pCR (p < 0.05). In contrast, a firm lesion and ulceration were frequently observed in patients with non-pCR (p = 0.02 and 0.05 respectively). Conclusions: Our study suggests that macroscopic pathologic features do correlate with pCR. Although cCR has comparable oncologic outcomes as pCR with favorable outcomes, standard criteria of cCR should first be defined so NCRT patients can safely be selected for observation only. [Table: see text]

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Totally neoadjuvant chemoradiation therapy with mFOLFOX6 in locally advanced rectal cancer: A single arm phase II study (FOTAC).

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.tps816 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. TPS816-TPS816

Author(s):

Jianwei Zhang ◽

Yue Cai ◽

Huabin Hu ◽

Jian Xiao ◽

Dianke Chen ◽

...

Keyword(s):

Rectal Cancer ◽

Phase Ii Study ◽

Locally Advanced ◽

Pathologic Complete Response ◽

Complete Response ◽

Advanced Rectal Cancer ◽

Locally Advanced Rectal Cancer ◽

Neoadjuvant Chemoradiation ◽

Chemoradiation Therapy ◽

Neoadjuvant Chemoradiation Therapy

TPS816 Background: Preoperative 5-Fluorouracil based chemoradiotherapy is the standard of treatment for locally advanced rectal cancer. About 15% to 18% of patients would achieve pathologic complete response (pCR) after 5-Fluorouracil based chemoradiation. And the survival outcome of patients with pCR was much better than that of non-pCR. In our previous FOWARC study, in the group of preoperative systemic chemotherapy with mFOLFOX6 combined with radiation, the pCR rate was up to 27.5%. In another study, adding mFOLFOX6 after neoadjuvant chemo radiation in locally advanced rectal cancer improve the pCR rate to 38%. This phase II study aimed to explore whether totally neoadjuvant chemoradiation therapy with mFOLFOX6 could further improve the pCR rate in locally advanced rectal cancer. Methods: The primary endpoint is the pathologic complete response rate (pCR).The secondary endpoint included 3-year disease free survival rate, 3-year local recurrence rate, and safety. We hypothesized that totally neoadjuvant chemoradiation therapy with mFOLFOX6 could improve the pCR rate from 18% to 45% with 5% type I error and 80% power. Fifty patients met inclusion criteria will be enrolled in the trial. All patients will receive long term radiation for 25 times and 50Gy before surgery. Four cycles of mFOLFOX6 would be performed every 2 weeks during radiotherapy, and another 4-6 cycles would be added after radiotherapy and before operation. Totally, the patients will receive 8-10 cycles of chemotherapy before surgery. MRI of the pelvic will be performed every 4 cycles of the therapy to assess clinical response. Then the patient will receive total mesorectal excision at least 8 weeks after radiotherapy. The post-operative chemotherapy will be omitted and all the patients go to surveillance. Clinical trial information: NCT02887313.

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Molecular diagnosis of pathologic complete response (pCR) to neoadjuvant chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC)

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2010.06.029 ◽

2010 ◽

Vol 211 (3) ◽

pp. S13-S14

Author(s):

Zhenbin Chen ◽

Wenyan Li ◽

Julio Garcia-Aguilar

Keyword(s):

Rectal Cancer ◽

Molecular Diagnosis ◽

Locally Advanced ◽

Pathologic Complete Response ◽

Complete Response ◽

Advanced Rectal Cancer ◽

Locally Advanced Rectal Cancer ◽

Neoadjuvant Chemoradiation ◽

Chemoradiation Therapy ◽

Neoadjuvant Chemoradiation Therapy

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Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI

Applied Sciences ◽

10.3390/app11209494 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9494

Author(s):

Seunghyun Lee ◽

Joonseok Lim ◽

Jaeseung Shin ◽

Sungwon Kim ◽

Heasoo Hwang

Keyword(s):

Rectal Cancer ◽

Dimension Reduction ◽

Pathologic Complete Response ◽

Prediction Method ◽

Complete Response ◽

Neoadjuvant Chemoradiation ◽

Chemoradiation Therapy ◽

Neoadjuvant Chemoradiation Therapy ◽

Deep Embedding ◽

Mri Sequences

Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on radiomics features or partially analyze multi-parametric MRI. We propose an effective pCR prediction method based on novel multi-parametric MRI embedding. We first seek to extract volumetric features of tumors that can be found only by analyzing multiple MRI sequences jointly. Specifically, we encapsulate multiple MRI sequences into multi-sequence fusion images (MSFI) and generate MSFI embedding. We merge radiomics features, which capture important characteristics of tumors, with MSFI embedding to generate multi-parametric MRI embedding and then use it to predict pCR using a random forest classifier. Our extensive experiments demonstrate that using all given MRI sequences is the most effective regardless of the dimension reduction method. The proposed method outperformed any variants with different combinations of feature vectors and dimension reduction methods or different classification models. Comparative experiments demonstrate that it outperformed four competing baselines in terms of the AUC and F1-score. We use MRI sequences from 912 patients with rectal cancer, a much larger sample than in any existing work.

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