Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

OBJECTIVES

To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases.

RESULTS

Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction.

CONCLUSION

Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC.

CLINICAL RELEVANCE STATEMENT

A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes.

KEY POINTS

• Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.

Predictors of toxicity after curative reirradiation with intensity modulated radiotherapy or proton therapy for recurrent head and neck carcinoma: new dose constraints for pharyngeal constrictors muscles and oral cavity

BACKGROUND

Our study aims to identify predictive factors of moderate to severe (grade ≥ 2) late toxicity after reirradiation (reRT) of recurrent head and neck carcinoma (HNC) and explore the correlations between dose organs at risk (OAR) and grade ≥ 2 toxicity.

MATERIAL AND METHODS

Between 09/2007 and 09/2019, 55 patients were re-irradiated with IMRT or proton therapy with curative intent for advanced HNC. Our study included all patients for whom data from the first and second irradiations were available. Co-variables, including interval to reRT, size of re-irradiated PTV, and dose to OAR, were analyzed as potential predictors for developing moderate to severe long-term toxicity with death as a competing risk. Receiver-operator characteristics (ROC) analysis assessed the association between dose/volume parameters and the risk of toxicity.

RESULTS

Twenty-three patients participated in our study. After a median follow-up of 41 months, 65% of the patients experienced grade ≥ 2 late toxicity. The average dose to pharyngeal constrictor muscles (PCM) at the time of reRT showed an association with the risk of grade ≥ 2 dysphagia: AUC = 0.78 (95% CI: 0.53-1), optimal cut-off value = 36.7 Gy (sensitivity 62%/specificity 100%). The average dose to the oral cavity at the time of reRT showed an association with the risk of grade ≥ 2 dysgeusia: AUC = 0.96 (0.89-1), optimal cut-off value = 20.5 Gy (sensitivity 100%/specificity 88%).

CONCLUSION

Our analysis depicted an association between the dose to OAR and the risk of developing moderate to severe dysphagia and dysgeusia and proposed new dose constraints for PCM (36.7 Gy) and oral cavity (20.5 Gy).

Curative high-dose reirradiation for patients with recurrent head and neck adenoid cystic carcinomas: outcomes and analysis of patterns of failure

BACKGROUND

To investigate the outcomes of patients who underwent curative reirradiation (reRT), with intensity-modulated radiation therapy (IMRT) or proton therapy (PT) for unresectable recurrent or second primary head and neck adenoid cystic carcinoma (HNACC).

METHODS

Ten patients, mostly KPS 90%, were reirradiated (3/10 with IMRT and 7/10 with PT) at a median maximum dose to the CTV of 64.2 Gy from July 2011 to November 2021. Locations at the time of reRT were mainly the sinus (4/10) and the salivary glands (including the parotid and submandibular gland, 3/10). CTCAEv5 was used to assess acute and late toxicities. Follow-up was the time between the end of reRT and the date of last news.

RESULTS

The median time between the two irradiations was 53.5 months (IQR: 18-84). After a median follow-up of 26 months (range, 12.5-51.8 months), six patients had developed a locoregional recurrence (LR), of which four occurred within the previously irradiated volume. Two and three-year locoregional failure-free survival (LFFS) and overall survival (OS) were 55.6% [95%CI: 31-99.7%], and 41% [18.5-94%] and 66.7% [42-100%] and 44.4% [21.4-92.3%], respectively. LFFS and OS were significantly better in the subgroup of sinus tumors (p = .013) and the subgroup of patients re-irradiated more than two years after the first course of irradiation (p = .01). Seven patients had impairments before the start of reRT, including hearing impairment (3/10) and facial nerve impairment (3/10). The most severe late toxicities were brain necrosis (2/10), osteoradionecrosis (1/10) and vision decreased (1/10).

CONCLUSION

Curative reRT for HNACC is possible for selected cases, but the LR rate in the irradiated field and the risk of severe toxicity remain high. Improved selection criteria and more carefully defined target volumes may improve outcome in these patients. A further study including larger cohort of patients would be useful to confirm these results.

[18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer

PURPOSE

To evaluate whether radiomics from [18F]-FDG PET and/or MRI before re-irradiation (reRT) of recurrent head and neck cancer (HNC) could predict the occurrence and the location "in-field" or "outside" of a second locoregional recurrence (LR).

METHODS

Among the 55 patients re-irradiated at curative intend for HNC from 2012 to 2019, 48 had an MRI and/or PET before the start of the reRT. Thirty-nine radiomic features (RF) were extracted from the re-irradiated GTV (rGTV) using LIFEx software. Student t tests and Spearman correlation coefficient were used to select the RF that best separate patients who recurred from those who did not, and "in-field" from "outside" recurrences. Principal component analysis involving these features only was used to create a prediction model. Leave-one-out cross-validation was performed to evaluate the models.

RESULTS

After a median follow-up of 17 months, 40/55 patients had developed a second LR, including 18 "in-field" and 22 "outside" recurrences. From pre-reRT MRI, a model based on three RF (GLSZM_SZHGLE, GLSZM_LGLZE, and skewness) predicted whether patients would recur with a balanced accuracy (BA) of 83.5%. Another model from pre-reRT MRI based on three other RF (GLSZM_ LZHGE, NGLDM_Busyness, and GLZLM_SZE) predicted whether patients would recur "in-field" or "outside" with a BA of 78.5%. From pre-reRT PET, a model based on four RF (Kurtosis, SUVbwmin, GLCM_Correlation, and GLCM_Contrast) predicted the LR location with a BA of 84.5%.

CONCLUSION

RF characterizing tumor heterogeneity extracted from pre-reRT PET and MRI predicted whether patients would recur, and whether they would recur "in-field" or "outside".