Tumor response and outcome after reverse treatment for patients with synchronous colorectal liver metastasis: a cohort study

Combined Hepatic Arterial Infusion Pump and Systemic Chemotherapy in the Modern Era for Chemotherapy-Naive Patients with Unresectable Colorectal Liver Metastases

PURPOSE

Chemotherapy-naive patients with unresectable colorectal liver metastases (CRLM) have been the best responders to hepatic arterial infusion (HAI) therapy. The current treatment paradigm has drifted away from HAI in the first-line setting. We aimed to analyze outcomes of combined first-line systemic therapy with HAI therapy (HAI+SYS) in the modern era.

METHODS

We conducted a retrospective study of consecutive chemotherapy-naive patients with unresectable CRLM who received HAI+SYS between 2003 and 2019. Patients were selected from a prospectively maintained database. Outcomes included radiological response rate, conversion to resection (CTR) rate, and overall survival (OS).

RESULTS

Fifty-eight chemotherapy-naive patients were identified out of 546 patients with unresectable CRLM managed with HAI. After induction treatment, 4 patients (7%) had a complete radiological response, including two durable responses. In total, 32 patients (55%) underwent CTR. CTR or complete response without resection was achieved after seven cycles of systemic therapy and four cycles of HAI therapy. Median OS for the whole cohort was 53.0 months (95% confidence interval 23.0-82.9). Three- and 5-year OS in patients who achieved CTR or complete response versus patients who did not was 88% and 72% versus 27% and 0% respectively. Of patients who underwent CTR, complete and major pathological response (no and <10% viable tumor cells, respectively) was observed in 7 (22%) and 12 patients (38%).

CONCLUSIONS

Combined HAI+SYS in chemotherapy-naive patients resulted in durable and substantial response in a large proportion of patients. Nearly two-thirds of patients achieved a complete response or proceeded to conversion surgery, which was associated with prolonged survival.

Long-term results of liver-first approach strategy in patients with advanced synchronous liver metastases from colorectal cancer

BACKGROUND

The "liver-first" approach (LFA) is a strategy indicated for advanced synchronous liver metastases (ASLM) from colorectal cancer (CRC). Includes neoadjuvant chemotherapy, resection of the ASLM followed by CRC resection.

METHODS

Retrospective descriptive analysis from a prospective database of hepatectomies from liver metastases (LM) from CRC in two centers. Between 2007-2019, 88 patients with CRC-ASLM were included in a LFA scheme. Bilobar (LM) was present in 65.9%, the mean number of lesions was 5.5 and mean size 42.7 mm. Response to treatment was assessed by RECIST criteria. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier survival curves.

RESULTS

Seventy-five of 88 patients (85.2%) completed the LFA. RECIST evaluation showed partial response in 75.7% and stable disease in 22.8%. Severe morbidity rate (Clavien-Dindo ≥ IIIA) after liver and colorectal surgery was present in 29.4% and 9.3%, respectively. There was no 90-day postoperative mortality in both liver and colorectal surgeries. Recurrence rate was 76%, being the liver the most frequent site, followed by the pulmonary. From the total number of recurrences (106) in 56 patients, surgical with chemotherapy rescue treatment was accomplished in 34 of them (32.1%). The mean PFS was 8.5 and 5-year OS was 53%.

CONCLUSIONS

In patients with CRC-ASLM the LFA allows control of the liver disease beforehand and an assessment of the tumor response to neoadjuvant chemotherapy, optimising the chance of potentially curative liver resection, which influences long-term survival.

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria

BACKGROUND

Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation.

OBJECTIVE

To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria.

METHODS

One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort.

RESULTS

In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34-0.84)].

CONCLUSION

The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.

Colorectal Cancer, Liver Metastases and Biotherapies

(1) Background: colorectal cancer (CRC) is one of the deadliest causes of death by cancer worldwide. Its first main metastatic diffusion spreads to the liver. Different mechanisms such as the epithelial-mesenchymal transition and angiogenesis are the characteristics of this invasion. At this stage, different options are possible and still in debate, especially regarding the use of targeted therapeutics and biotherapies. (2) Methods: A review of the literature has been done focusing on the clinical management of liver metastasis of colorectal cancer and the contribution of biotherapies in this field. (3) Results: In a clinical setting, surgeons and oncologists consider liver metastasis in CRC into two groups to launch adapted therapeutics: resectable and non-resectable. Around these two entities, the combination of targeted therapies and biotherapies are of high interest and are currently tested to know in which molecular and clinical conditions they have to be applied to impact positively both on survival and quality of life of patients.

Granzyme B PET Imaging of Combined Chemotherapy and Immune Checkpoint Inhibitor Therapy in Colon Cancer

Purpose

Chemotherapeutic adjuvants, such as oxaliplatin (OXA) and 5-fluorouracil (5-FU), that enhance the immune system, are being assessed as strategies to improve durable response rates when used in combination with immune checkpoint inhibitor (ICI) monotherapy in cancer patients. In this study, we explored granzyme B (GZB), released by tumor-associated immune cells, as a PET imaging-based stratification marker for successful combination therapy using a fluorine-18 (¹⁸F)-labelled GZB peptide ([¹⁸F]AlF-mNOTA-GZP).

Methods

Using the immunocompetent CT26 syngeneic mouse model of colon cancer, we assessed the potential for [¹⁸F]AlF-mNOTA-GZP to stratify OXA/5-FU and ICI combination therapy response via GZB PET. In vivo tumor uptake of [¹⁸F]AlF-mNOTA-GZP in different treatment arms was quantified by PET, and linked to differences in tumor-associated immune cell populations defined by using multicolour flow cytometry.

Results

[¹⁸F]AlF-mNOTA-GZP tumor uptake was able to clearly differentiate treatment responders from non-responders when stratified based on changes in tumor volume. Furthermore, [¹⁸F]AlF-mNOTA-GZP showed positive associations with changes in tumor-associated lymphocytes expressing GZB, namely GZB+ CD8+ T cells and GZB+ NK+ cells.

Conclusions

[¹⁸F]AlF-mNOTA-GZP tumor uptake, driven by changes in immune cell populations expressing GZB, is able to stratify tumor response to chemotherapeutics combined with ICIs. Our results show that, while the immunomodulatory mode of action of the chemotherapies may be different, the ultimate mechanism of tumor lysis through release of Granzyme B is an accurate biomarker for treatment response.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11307-021-01596-y.