Abstract
Background
Histopathologic assessment of tumor viability has emerged as a potential predictive factor of outcomes in various cancers. This study evaluates the prognostic significance of viability in high-grade soft tissue sarcoma while accounting for different adjuvant regimens and clinical variables.
Methods
A retrospective chart review was conducted on 147 patients surgically treated for high-grade soft tissue sarcoma between 2010 and 2021 at a single institution. Perioperative, clinical and surveillance data were collected. Tumor viability was determined through histopathologic analysis by a board-certified pathologist.
Results
No significant differences in clinical variables were observed between groups with ≤10 % and >10 % tumor viability. Neoadjuvant treatments, tumor grade, size, and depth did not independently affect tumor viability. There was no statistically decreased risk of local recurrence in the group with ≤10 % viability compared to the group with >10 % viability (HR = 1.19, 95 % CI [0.57,2.50]) (p = 0.64). Margin status was the only variable that significantly increases the risk of LR on multivariate analysis.
Conclusion
This cohort suggests that neoadjuvant radiotherapy, chemotherapy, or their combination did not influence tumor viability predictably. Notably, tumors without neoadjuvant treatment exhibited a high rate of necrosis, potentially confounding the interpretation of treatment effect. Other factors such as tumor type may play a more significant role in the cause of tumor necrosis than originally thought. Pathologic tissue response continues to offer value for the management of STS, but these findings underscore the need for further investigation into tumor viability in soft tissue sarcoma, targeting specific treatments analyzed in large collaborative studies.
Keywords: Tumor viability, Necrosis, Soft tissue sarcoma, Metastasis
1. Introduction
Soft tissue sarcomas are part of a group of rare malignancies that arise from mesenchymal cells.1 The most common tissue sites of origin include muscle, connective and adipose tissue.1 Soft tissue sarcomas have a high level of heterogeneity given that more than 50 different types have been identified.1 The incidence of soft tissue sarcomas is roughly 6 per 100,000 and the incidence in males is nearly twice that of females.2 Local control with modern adjuvant radiation therapy and surgery is effective, approximately 90 %. High grade soft tissue sarcoma is particularly lethal with a median survival time of roughly 47 months in patients with grade III tumors. This number varies greatly based on tumor subtype and location.2, 3, 4 Even for those individuals that survive these high-grade tumors, treatment itself may have a significantly adverse impact on quality of life.5,6
Histopathologic tumor viability, defined as the percent of tumor that is viable with or without neoadjuvant therapy, has emerged as a potential predictive factor of outcomes in multiple types of cancers.7, 8, 9 The importance of tumor viability has been established in primary bone sarcomas, showing that viability rates greater than 10 % are associated with increased rates of local recurrence and distant metastasis.10,11 The importance of tumor viability is still debated for primary soft tissue sarcomas. Some studies support the notion that tumor viability after neoadjuvant chemotherapy may be an independent predictor of prognosis, with more viability indicating a worse prognosis.12, 13, 14, 15 Other, more recent, studies demonstrate no association between tumor viability and prognosis in patients with soft tissue sarcomas.16, 17, 18, 19
The purpose of our study was to assess the impact of different neoadjuvant regimens on tumor viability in the setting of high-grade soft tissue sarcoma. We then measured the impact of each treatment course and tumor viability on local recurrence, distant metastasis, and mortality. A multivariate analysis was conducted to control for various factors that may influence local recurrence, to more accurately assess the impact of different neoadjuvant treatment courses on the value of tumor viability as a prognostic factor.
2. Methods
This is a retrospective cohort study of 147 patients treated surgically for soft tissue sarcoma at the same institution from 2010 to 2021. All patients treated for a grade 2 or 3 soft tissue sarcoma were included. Patients with metastatic disease at presentation, low-grade sarcoma, head & neck tumors, retroperitoneal tumors and patients managed nonoperatively were excluded from this study. The study was approved by our institutional IRB.
2.1. Demographics and clinical data
Demographic and clinical data was collected for each patient including patient age, gender, race, ethnicity, body mass index (BMI), presence of diabetes, smoking status, treatment with preoperative or postoperative chemotherapy and radiotherapy in the form of external beam radiation (EBRT) or brachytherapy (BT). The chemotherapy regimen consisted of a standardized doxorubicin and ifosfomide treatment with rare exceptions. All patients treated with brachytherapy received care from a single radiation oncologist (MK) with specialization in brachytherapy. Brachy-therapy catheters were implanted intraoperatively immediately after surgical-wide excision and subsequently loaded, on average, 5 days post-operatively, achieving a peripheral tumor dose of 34 Gy. EBRT was given with three-dimensional conformal radiation therapy and more recently with intensity-modulated radiation therapy, in accordance with previously described techniques; typically, 50 Gy. Surgery was completed within 1 month after completion of radiation therapy. Additionally, tumor characteristics were recorded for each patient including tumor type, size, grade, location, viability, and resection margins. Tumor viability was defined as the percent of viable tumor cells at the time of tumor resection. The authors used the French Federation of Cancer Centers Sarcoma Group (FNCLCC) grading system. At minimum, one section per centimeter of tumor was processed for microscopic examination, the extent of tumor necrosis was estimated for each specimen, and then a total was calculated as a final percentage. All tumor necrosis was determined by a single MSK trained pathologist. Indication for treatment modality was carefully determined by a multidisciplinary musculoskeletal oncology team. Additionally, tumor characteristics were recorded for each patient including tumor type, size, grade, location, viability, and resection margins. Tumor viability was defined as the percent of viable tumor cells at the time of tumor resection. Patients with synovial sarcoma, clear-cell sarcoma, angiosarcoma, rhabdomyosarcoma, and epithelioid sarcoma (SCARE) were identified and specifically assessed, as these types of sarcomas are most likely to metastasize to locoregional lymph nodes.20 Percent viability of the tumor was grouped by ≤ 10 % and >10 % consistent with the literature.13,21 In the case of large soft tissue defect after the resection, the need for complex soft tissue reconstructive procedures including locoregional or free flaps was documented. Clinical outcomes were recorded by the primary musculoskeletal oncology attending surgeon including local recurrence, distant metastasis, any complications, and mortality.
2.2. Statistical analyses
Descriptive statistics were utilized to compare different neoadjuvant regimens to provide an understanding of their impact on tumor viability, local recurrence, distant metastasis, and overall survival. Neoadjuvant treatment groups were stratified into no neoadjuvant therapy, chemotherapy monotherapy, radiation monotherapy, and both chemotherapy and radiation therapy. Quantitative variables are expressed as mean and standard deviation or median and interquartile range depending on their distribution. Categorical variables are expressed as absolute values or percentages. Comparative analyses, including chi-square or Fisher's exact tests and t-tests or Mann-Whitney U tests, were applied to explore associations and differences in categorical and continuous variables, respectively. Subgroup analyses further stratify the data based on tumor subtype, grade, and patient demographics to investigate potential variations in the impact of different neoadjuvant regimens. Multivariate regression, using Cox models and logistic regression, were utilized to identify independent predictors of overall survival and assess factors associated with specific outcomes. Survival analysis techniques, such as Kaplan-Meier curves, log-rank tests, and Cox Proportional-Hazards models, were employed to estimate overall survival rates and assess differences between different neoadjuvant treatment groups, while adjusting for relevant covariates. Data were analyzed using STATA version 18.0 (STATA Corp, USA) and R version 3.5.1 (R Foundation for Statistical Computing). Statistical significance was defined as a p-value less than 0.05.
3. Results
A total of 147 patients were enrolled in this study. 78 patients had >10 % viable tumor (53 %), while 69 patients had ≤10 % viable tumor (47 %). The mean age at the time of surgery was similar between groups with mean of 55.9 years for patients with >10 % viable tumor (SD 17.1) and 53.0 years (SD 17.0) for patients with ≤10 % viability (p = 0.32). There was a significant difference in gender distribution with 51 males (65.4 %), in the group with >10 % viability, compared to 32 males (46.4 %), in the group of patients with ≤10 % viability (p = 0.03). Average Body Mass Index (BMI) was also similar between groups with an average of 28.6 (SD 7.7) in the group with >10 % viability, and 28.4 (SD 5.8) in the group with ≤10 % viability (p = 0.90). The prevalence of diabetes was similar between groups with 26.9 % in the group with >10 % viability, and 20.3 % in the group with ≤10 % viability (p = 0.45) (see Table 1).
Table 1.
Descriptive analysis.
| Variable | Total n = 147 Mean (std)/n (%) | A (Viable >10 %) n = 78 Mean (std)/n (%) | B (≤10 %). n = 69 Mean (std)/n (%) | p-value | |||
|---|---|---|---|---|---|---|---|
| Age | 54.5 | 17.00 | 55.9 | 17.06 | 53.0 | 16.95 | 0.32 |
| Gender | 0.03 | ||||||
| M | 83 | 56.5 | 51 | 65.4 | 32 | 46.4 | |
| F | 64 | 43.5 | 27 | 34.6 | 37 | 53.6 | |
| BMI | 28.5 | 6.8 | 28.6 | 7.7 | 28.4 | 5.7 | 0.90 |
| Diabetes | 0.45 | ||||||
| Yes | 35 | 23.8 | 21 | 26.9 | 14 | 20.3 | |
| Size | 0.92 | ||||||
| <5 cm | 26 | 66.7 | 14 | 17.9 | 12 | 17.4 | |
| 5–10 cm | 55 | 0.7 | 28 | 36.9 | 27 | 39.1 | |
| >10 cm | 66 | 32.7 | 36 | 46.2 | 30 | 43.5 | |
| Depth | 0.56 | ||||||
| Deep | 45 | 30.6 | 26 | 33.3 | 19 | 27.5 | |
| Superficial | 102 | 69.4 | 52 | 66.7 | 50 | 72.5 | |
| Specific types | |||||||
| SCARE | 28 | 19.0 | 17 | 21.8 | 11 | 15.9 | 0.49 |
| MFS | 26 | 17.7 | 14 | 17.9 | 12 | 17.4 | 0.99 |
| All Others | 93 | 63.3 | 47 | 60.3 | 46 | 66.7 | 0.53 |
| Grade | 0.17 | ||||||
| II | 42 | 28.6 | 18 | 23.1 | 24 | 34.8 | |
| III | 105 | 71.4 | 60 | 76.9 | 45 | 65.2 | |
| Neoadj treatment | 0.30 | ||||||
| No Chemo, No Rad | 60 | 40.8 | 30 | 38.5 | 30 | 43.5 | |
| Chemo, No Rad | 33 | 22.4 | 20 | 25.6 | 13 | 18.8 | |
| No Chemo, Rad | 27 | 18.4 | 11 | 14.1 | 16 | 23.2 | |
| Chemo, Rad | 27 | 18.4 | 17 | 21.8 | 10 | 14.5 | |
| Fungating | 0.77 | ||||||
| Yes | 12 | 8.2 | 7 | 9.0 | 5 | 7.2 | |
| Margins | 0.74 | ||||||
| Positive | 9 | 6.1 | 4 | 5.1 | 5 | 7.2 | |
| Clinical Scenario | 0.03 | ||||||
| No Chemo, Rad | 41 | 27.9 | 16 | 20.5 | 25 | 39.2 | |
| Chemo, Rad | 94 | 63.9 | 52 | 66.7 | 42 | 60.9 | |
| Chemo, No Rad | 11 | 7.5 | 9 | 11.5 | 2 | 2.9 | |
| No Treatment | 1 | 0.7 | 1 | 1.3 | 0 | 0.0 | |
| Recurrence | |||||||
| LR | 28 | 19 | 14 | 17.9 | 14 | 20.3 | 0.88 |
| All Metastasis | 15 | 10.2 | 10 | 12.8 | 5 | 7.2 | 0.40 |
| Lung Metastasis | 10 | 6.8 | 8 | 10.3 | 2 | 2.9 | 0.10 |
| Nodal Metastasis | 5 | 3.4 | 2 | 2.6 | 3 | 4.3 | 0.67 |
| Status | 0.03 | ||||||
| NED | 112 | 76.2 | 62 | 79.5 | 50 | 72.5 | |
| AWD | 13 | 8.8 | 2 | 2.6 | 11 | 15.9 | |
| DOD | 10 | 6.8 | 6 | 7.7 | 11 | 5.8 | |
| DOC | 12 | 8.2 | 8 | 10.3 | 4 | 5.8 | |
| Follow-up (Months) | 64.88 | 31.65 | 65.79 | 30.96 | 63.85 | 32.61 | 0.71 |
Tumor size, which was classified as less than 5 cm, 5–10 cm, and more than 10 cm, did not show significant differences between groups (p = 0.92). Depth of tumor did not demonstrate significant findings (p = 0.56). There were no differences in the prevalence of SCARE sarcomas (p = 0.49) or myxofibrosarcomas (p = 0.99). The distribution of grade II and grade III tumors was not statistically different between groups, with 60 patients (76.9 %) demonstrating grade III tumors in the group with >10 % viability, and 45 patients (65.2 %) demonstrating grade III tumors in the group with ≤10 % viability (p = 0.17). In patients with >10 % viability, 9.0 % were fungating, and in patients with ≤10 % viability, 7.2 % were fungating, with no difference in prevalence between groups (p = 0.77). In patients with >10 % viability 5.1 % had positive surgical margins, and in patients with ≤10 % viability 7.2 % had positive surgical margins, with no difference in incidence between groups (p = 0.74). There was no difference in the distribution of neoadjuvant therapies distribution between patients in the ≤10 % viability group and the >10 % viability group (p = 0.30).
The number of patients with local recurrence was similar in the group with >10 % viability compared to ≤10 % viability, being 17.9 % and 20.3 % respectively (p = 0.88). The number of patients with metastases was similar in the group with >10 % viability compared to ≤10 % viability, being 12.8 % and 7.2 % respectively (p = 0.40). Patient's status at last follow-up was significantly different between groups (p = 0.03), with 79.5 % of patients as No Evidence of Disease (NED), 2.6 % Alive with Disease (AWD), 7.7 % Dead of Disease (DOD), and 10.3 % Dead of Other Causes (DOC) in the group with >10 % viability compared to 72.5 % of patients NED, 15.9 % AWD, 5.8 % DOD, and 5.8 % DOC in the group with ≤10 % viability. The average follow-up duration in months did not significantly differ between groups, with an average of 65.8 months in the group with >10 % viability, 63.9 months in the group with ≤10 % viability (p = 0.71) (see Table 2).
Table 2.
Descriptive Analysis for neoadjuvant naive patients.
| Variable | Total n = 60 Mean (std)/n (%) | A (Viable >10 %), n = 30 Mean (std)/n (%) | B (≤10 %) n = 30 Mean (std)/n (%) | p-value | |||
|---|---|---|---|---|---|---|---|
| Age | 55.1 | 19.1 | 57.1 | 19.6 | 53.1 | 18.8 | 0.42 |
| Gender | 0.30 | ||||||
| M | 29 | 48.3 | 17 | 56.7 | 12 | 40.0 | |
| F | 31 | 51.7 | 13 | 43.3 | 18 | 60.0 | |
| BMI | 28.7 | 7.3 | 28.7 | 8.0 | 28.7 | 6.7 | 0.99 |
| Diabetes | 0.40 | ||||||
| Yes | 18 | 30.0 | 11 | 36.7 | 7 | 23.3 | |
| Size | 0.37 | ||||||
| <5 cm | 15 | 25.0 | 5 | 16.7 | 10 | 33.3 | |
| 5–10 cm | 19 | 31.7 | 11 | 36.7 | 8 | 26.7 | |
| >10 cm | 26 | 43.3 | 14 | 46.6 | 12 | 40.0 | |
| Depth | 0.99 | ||||||
| Deep | 15 | 25.0 | 8 | 26.7 | 7 | 23.3 | |
| Superficial | 45 | 75.0 | 22 | 73.3 | 23 | 76.7 | |
| Specific types | |||||||
| SCARE | 14 | 23.3 | 10 | 33.3 | 4 | 13.3 | 0.13 |
| MFS | 9 | 15.0 | 4 | 13.3 | 5 | 16.7 | 0.99 |
| All Others | 37 | 61.7 | 16 | 53.3 | 21 | 70.0 | 0.29 |
| Grade | 0.25 | ||||||
| II | 17 | 28.3 | 6 | 20.0 | 11 | 36.7 | |
| III | 43 | 71.7 | 24 | 80.0 | 19 | 63.3 | |
| Fungating | 0.99 | ||||||
| Yes | 2 | 3.3 | 1 | 3.3 | 1 | 3.3 | |
| Margins | 0.99 | ||||||
| Positive | 7 | 11.7 | 3 | 10.0 | 4 | 13.3 | |
| Clinical Scenario | 0.14 | ||||||
| No Chemo, Rad | 26 | 43.3 | 9 | 30.0 | 17 | 56.7 | |
| Chemo, Rad | 29 | 48.3 | 17 | 56.7 | 12 | 40.0 | |
| Chemo, No Rad | 4 | 6.7 | 3 | 10.0 | 1 | 3.3 | |
| No Treatment | 1 | 1.7 | 1 | 3.3 | 0 | 0.0 | |
| Recurrence | |||||||
| LR | 14 | 23.3 | 5 | 16.7 | 9 | 30.0 | 0.36 |
| All Metastasis | 7 | 11.7 | 3 | 10.0 | 4 | 13.3 | 0.99 |
| Lung Metastasis | 5 | 8.3 | 3 | 10.0 | 2 | 6.7 | 0.99 |
| Nodal Metastasis | 2 | 3.3 | 0 | 0.0 | 2 | 6.7 | 0.49 |
| Status | 0.02 | ||||||
| NED | 45 | 75.0 | 24 | 80.0 | 21 | 70.0 | |
| AWD | 6 | 10.0 | 0 | 0.0 | 6 | 20.0 | |
| DOD | 6 | 10.0 | 3 | 10.0 | 3 | 10.0 | |
| DOC | 3 | 5.0 | 3 | 10.0 | 0 | 0.0 | |
| Follow-up (Months) | 66.61 | 35.32 | 62.12 | 35.12 | 71.11 | 35.53 | 0.33 |
A descriptive analysis was then performed on patients who were naïve to both neoadjuvant chemotherapy and radiotherapy treatments, following the same structure as the descriptive analysis on performed in Table 1. This too demonstrate that tumor size (p = 0.37), depth (p = 0.99), grade (p = 0.25), or fungating tumors (p = 0.99), SCARE tumors (p = 0.125), and MFS (p = 0.99), were all similar between patients with ≤10 % viability and >10 % viability. The incidence of local recurrence in the ≤10 % viability group was 30 % compared to 16.7 % in the >10 % viability group, although this was not statistically significant (p = 0.36). Additionally, the incidence of distant metastasis (p = 0.99), lung metastasis (p = 0.99), and nodal metastasis (p = 0.492) were similar between groups. Just as in the complete cohort, in the neoadjuvant naïve cohort, patient status at last follow-up was significantly different between groups (p = 0.02). In the group with >10 % viability, 80 % of patients as No Evidence of Disease (NED), 0 % Alive with Disease (AWD), 10 % Dead of Disease (DOD), and 10 % Dead of Other Causes (DOC) compared to 70 % of patients NED, 20.0 % AWD, 10 % DOD, and 0 % DOC in the group with ≤10 % viability (see Table 3).
Table 3.
Pathologic response (≤10 % viability) Univariate Logistic Regression.
| Variable | OR | 95 % C.I | p-value | |
|---|---|---|---|---|
| Neoadj treatment | 0.29 | |||
| No Rad, No Chemo | 1 | |||
| No Rad, Chemo | 0.65 | (0.27; 1.53) | ||
| Rad, No Chemo | 1.45 | (0.58; 3.72) | ||
| Rad & Chemo | 0.59 | (0.23; 1.47) | ||
| Age (per group) | 0.29 | |||
| A: <60 | 1 | |||
| B: ≥60 | 0.70 | (0.36; 1.35) | ||
| Gender | 0.02 | |||
| Male | 1 | |||
| Female | 2.18 | (1.13; 4.28) | ||
| Size | 0.92 | |||
| <5 cm | 1 | |||
| 5–10 cm | 1.13 | (0.44; 2.90) | ||
| >10 cm | 0.97 | (0.39; 2.44) | ||
| Depth | 0.45 | |||
| Deep | 1 | |||
| Superficial | 1.32 | (0.65; 2.69) | ||
| Type of STS | ||||
| SCARE | 0.68 | (0.29; 1.56) | 0.37 | |
| MFS | 0.96 | (0.41; 2.25) | 0.93 | |
| All Others | 1.32 | (0.67; 2.61) | 0.42 | |
| Grade | ||||
| II | 1 | 0.12 | ||
| III | 0.56 | (0.27; 1.15) | ||
| Fungating | ||||
| Positive | 0.79 | (0.22; 2.60) | 0.70 | |
Univariate analysis showed that neoadjuvant treatment did not impact pathologic tumor response (≤10 % viability) when compared to no neoadjuvant treatment (p = 0.29). Age ≥60 was not associated with an increased risk of pathologic response when compared to age <60 (p = 0.29). Female sex was associated with a significantly increased risk of pathologic response when compared to male sex (HR = 2.18, 95 % CI [1.13, 4.28]) (p = 0.02). Tumor size of >10 cm and 5–10 cm were not associate with an increased risk for pathologic response when compared to <5 cm (p = 0.92). Superficial tumors were not associated with an increased risk of pathologic response when compared to deep seated tumor (p = 0.45). SCARE sarcomas and MFS were not associated with an increased risk of pathologic response when compared to all other tumors (p = 0.37 and 0.93, respectively). Grade III tumors were not associated with an increased risk of pathologic response when compared to grade II tumors (p = 0.12). Fungating tumors were not associated with an increased risk of pathologic response when compared to non-fungating tumors (p = 0.70) (see Table 4).
Table 4.
Path Response (≤10 % viable) Multivariate Regression model.
| VARIABLE | OR | (95%CI) | p-value | |
|---|---|---|---|---|
| Grade | II | 1 | 0.12 | |
| III | 0.61 | (0.29; 1.26) | ||
| Size | less than 5 cm | 1 | 0.94 | |
| between 5 and 10 | 1.20 | (0.46; 3.19) | ||
| greater than 10 | 1.13 | (0.44; 2.94) | ||
| Depth | Deep | 1 | 0.56 | |
| Superficial | 1.18 | (0.56; 2.47) | ||
| Neoadjuvant Radiation | No | 1 | 0.92 | |
| Yes | 1.13 | (0.56; 2.29) | ||
| Neoadjuvant Chemo | No | 1 | 0.11 | |
| Yes | 0.56 | (0.28; 1.13) |
Multivariate regression cox analysis explored the impact of multiple important independent associations of several variables with the hazard of pathologic response in this specific context. When controlled for grade, size, and tumor depth, neoadjuvant radiation therapy (HR = 1.13, 95 % CI [0.56, 2.29]; p = 0.92) and neoadjuvant chemotherapy (HR = 0.56, 95 % CI [0.28, 1.13]; p = 0.11) were both not associated with an increased risk for pathologic tumor response when compared to no neoadjuvant radiation or chemotherapy (see Table 5).
Table 5.
Local recurrence univariate regression.
| Variable | HR | 95 % C.I | p-value | |
|---|---|---|---|---|
| Setting | ||||
| Rad, No Chemo | 1 | 0.38 | ||
| Rad & Chemo | 0.69 | (0.30; 1.59) | ||
| Viability (% or Groups) | ||||
| A: >10 % | 1 | 0.64 | ||
| B: ≤10 % | 1.19 | (0.57; 2.50) | ||
| Size | 0.33 | |||
| <5 cm | 1 | |||
| 5–10 cm | 1.41 | (0.38; 5.22) | ||
| >10 cm | 2.21 | (0.64; 7.58) | ||
| Depth | 0.01 | |||
| Deep | 1 | |||
| Superficial | 6.54 | (1.55; 27.58) | ||
| Type of STS | ||||
| SCARE | 0.31 | (0.07; 1.30) | 0.11 | |
| MFS | 1.96 | (0.86; 4.46) | 0.11 | |
| Grade | ||||
| II | 1 | 0.68 | ||
| III | 0.84 | (0.38; 1.87) | ||
| Margins | ||||
| Positive | 17.35 | (7.69; 39.14) | 0.0001 | |
| Planned vs Unplanned | ||||
| Planned | 0.15 | (0.07; 0.36) | 0.0001 | |
| Fungating | ||||
| Positive | 3.93 | (1.59; 9.73) | 0.003 | |
Univariate analysis showed that patients treated with radiation monotherapy demonstrated no difference in risk of local recurrence compared to those treated with combined radiation and chemotherapy (HR = 0.69, 95 % CI [0.30,1.59]) (p = 0.38). There was no statistically increased risk of local recurrence in the group with ≤10 % viability compared to the group with >10 % viability (HR = 1.19, 95 % CI [0.57,2.50]) (p = 0.64). Tumor size and local recurrence showed no significant relationship (p = 0.33), but superficial tumors were associated with an increased risk of local recurrence when compared to deep-seated tumors (HR = 6.54, 95 % CI [1.55, 27.58]) (p = 0.01). Specific STS type (SCARE and MFS) did not show a significant association with local recurrence (p = 0.11). Grade also did not show a significant association with local recurrence (p = 0.68). Positive margins were associated with an increase in risk for local recurrence (HR = 17.35, 95 % CI [7.69, 39.14]) (p = 0.0001). Planned excision was associated with a significant decrease in risk for local recurrence compared to unplanned excisions (HR = 0.15, 95 % CI [0.07, 0.36]) (p = 0.0001). Fungating mass was associated with an increased risk for local recurrence (HR = 3.93, 95 % CI [1.59, 9.73]) (p = 0.003) (see Table 6).
Table 6.
LR Multivariate Regression model.
| VARIABLE | HR | (95%CI) | p-value | |
|---|---|---|---|---|
| Grade | II | 1 | 0.50 | |
| III | 0.72 | (0.28; 1.88) | ||
| Size | less than 5 cm | 1 | 0.70 | |
| between 5 and 10 | 1.37 | (0.28; 6.77) | ||
| greater than 10 | 1.87 | (0.39; 8.98) | ||
| Margins | Neg | 1 | 0.0001 | |
| Pos | 22.67 | (8.05; 63.93) | ||
| Viability | >10 | 1 | 0.41 | |
| ≤10 % | 1.44 | (0.61; 3.43) | ||
| Clinical scenario | Just Rad, No Chemo | 1 | 0.64 | |
| Rad & Chemo | 0.81 | (0.33; 1.98) |
Multivariate regression cox analysis explored the impact of multiple important independent associations of several variables with the hazard of local recurrence in this specific context. When controlled for grade, size, % viability, and clinical scenario, positive margins were predictive of local recurrence event (HR = 17.85, 95 % CI [6.53,48.83) (p=0.0001) when compared to negative margins. Controlling for these factors, patients with ≤10 % viability did not demonstrate a decreased risk of local recurrence in our cohort (HR = 1.35, 95 % CI [0.6, 3.06]) (p = 0.47) (see Table 7).
Table 7.
Subanalysis - Local Recurrence Univariate Regression on neoadjuvant naïve cohort.
| Variable | HR | 95 % C.I | p-value | |
|---|---|---|---|---|
| Clinical Setting | ||||
| Rad, No Chemo | 1 | 0.93 | ||
| Rad & Chemo | 1.07 | (0.23; 4.96) | ||
| Viability | 0.23 | |||
| >10 % | 1 | |||
| ≤10 % | 1.96 | (0.66; 5.86) | ||
| Size | 0.43 | |||
| <5 cm | 1 | |||
| 5–10 cm | 1.70 | (0.30; 9.29) | ||
| >10 cm | 2.54 | (0.54; 12.00) | ||
| Depth | 0.13 | |||
| Deep | 1 | |||
| Superficial | 4.75 | (0.62; 36.36) | ||
| Type of STS | ||||
| SCARE | 0.25 | (0.03; 1.88) | 0.18 | |
| MFS | 2.80 | (0.88; 8.96) | 0.08 | |
| Grade | ||||
| II | 1 | 0.88 | ||
| III | 0.92 | (0.29; 3.00) | ||
| Margins | ||||
| Positive | 15.52 | (5.39; 46.3) | 0.0001 | |
| Planned vs Unplanned | ||||
| Planned | 0.24 | (0.08; 0.77) | 0.02 | |
| Fungating | ||||
| Positive | 3.14 | (0.42; 24.5) | 0.26 | |
Univariate analysis was performed for local recurrence on patients who were naïve to both neoadjuvant chemotherapy and radiotherapy treatments. Univariate analysis showed that there was no significant difference in the risk of local recurrence in the setting of adjuvant chemotherapy and radiation dual therapy when compared to adjuvant radiation monotherapy (HR = 1.07, 95 % CI [0.23, 4.96]; p = 0.93). There was no significant difference in the risk of LR in ≤10 % viability tumors (HR = 1.96 95 % CI [0.66, 5.86]) when compared to >10 % viable tumors (p = 0.23). Tumor size showed no significant association with tumor recurrence (p = 0.43). Similarly, depth also showed no significant difference in risk of local recurrence (p = 0.13) (see Table 8). SCARE STS showed no significant association with LR (HR = 0.25, 95 % CI [0.03, 1.88]; p = 0.18) and Myxofibrosarcomas also showed no significant association with LR (HR = 2.80 95 % CI [0.88, 8.96]; p = 0.08). Grade showed no significant association with LR (p = 0.08), but positive margins were associated with a significantly increased risk for LR (HR = 15.52, 95 % CI [5.39, 46.3]; p=0.0001) when compared to negative margins. Similarly, planned excisions were associated with a significantly decreased risk of LR (HR = 0.24, 95 % CI [0.08, 0.77]; p= 0.02) when compared to unplanned excision. Fungating mass did not reveal significant associations with local recurrence (p = 0.26) (see Fig. 1).
Table 8.
Types of sarcomas.
| Sarcoma Type | N |
|---|---|
| Liposarcoma | 30 |
| Synovial Sarcoma | 11 |
| Leiomyosarcoma | 12 |
| Undifferentiated Pleiomorphic Sarcoma | 35 |
| Fibrosarcoma | 7 |
| Dermatofibrosarcoma Protuberans | 6 |
| Myxofibrosarcoma | 24 |
| Clear Cell Sarcoma | 0 |
| Angiosarcoma | 4 |
| Rhabdomyosarcoma | 3 |
| Epithelioid Sarcoma | 3 |
| Other | 9 |
Fig. 1.
Local Recurrence Survival Curves. 1.1 Global Viability Groups LR Survival curves. 1.2 Neoadjuvant Naïve Viability groups LR curves.
4. Discussion
Tumor histologic analysis is thought to be a crucial aspect of modern cancer therapy, further guiding prognosis and various treatment protocols. In soft tissue sarcoma, histologic tumor cell viability analysis is used to guide treatment; however, various studies have not clearly elucidated its impact on prognosis. Various treatment modalities can impact the histology at the time of resection, as do intrinsic tumor factors i.e. tumor subtypes and rate of growth. This study assesses the impact of the tumor viability at the time of resection on local recurrence, distant metastasis, and patient overall survival. In this cohort, patients with ≤10 % viability did not demonstrate a lower risk of LR, DM, and OS compared to patients with >10 % viability. In fact, a large number of tumors that did not receive neoadjuvant therapy exhibited tumor necrosis. These results, similar to studies performed by Mullen et al. and Menendez et al. demonstrate that in soft tissue sarcoma, percent viability at the time of excision does not significantly predict LR, DM, and OS.19,22
Neoadjuvant therapies had no demonstrable effect on tumor viability at the time of excision. When there was no neoadjuvant treatment, 50 % of tumors had a less than 10 % viable cells, while chemotherapy monotherapy had 39 %, radiation monotherapy had 62 %, and dual therapy had 37 %. This conclusion is similar to that described in a 2018 review study by Salah et al. that assessed 21 retrospective analyses and determined that only isolated limb perfusion therapy was associated with increased tumor necrosis.13 Other studies refute this finding. Fromm et al. determined that both chemotherapy and radiation neoadjuvant therapy enhanced tumor necrosis rates in STS, however, they note that untreated patients still present with tumors that display significant necrosis due to the rate of growth.17 Other studies report similar observations.21,23 Given that 50 % of patients in the cohort that received no prior treatment had tumors with greater than 90 % necrosis, the true impact of neoadjuvant therapy is on tissue necrosis is uncertain. Perhaps other factors play a more significant role in determining pathologic response than the neoadjuvant therapy itself. One factor may be the specific characteristics of the neoadjuvant regimens. A prospective study by Pisters et al. showed that histopathologic response was positively associated with doxorubicin dose given together with radiotherapy.24 Eilber et al. showed that tumor necrosis increased to a much greater level with the use of ifosfamide compared to all other standard protocols.25 Histopathological tumor type and variable rates of tumor growth may also influence necrosis rates. Studies have shown that liposarcomas and leiomyosarcomas have a greater pathological response to neoadjuvant therapy than those such as synovial sarcomas and other rare STS.26 Our cohort had nearly 40 % of SCARE sarcomas and MFS which can potentially influence therapy response. Future studies isolating specific characteristics of both chemotherapy and radiation therapy regimens is necessary to determine their true impact on tumor necrosis.
This study demonstrates that tumor size is not significantly associated with tumor viability at the time of resection, which has been demonstrated in the literature.27 Fromm et al. similarly reported that when all neoadjuvant treatments are considered, size does not impact necrosis rate17 although, other studies suggest that larger tumors exhibit more tumor necrosis.28 The effect of tumor grade on histologic viability following neoadjuvant treatment has not been elucidated in the setting of soft tissue sarcoma. In various tumors such as breast cancer, higher grade (Grade III) tumors are associated with an increased tumor response after neoadjuvant therapy, while in rectal cancer lower tumor grade is associated with an increased pathologic complete response following neoadjuvant therapy.29,30 In our study, high-grade (II and III) STS tumors did not seem to exhibit a statistically significant pathological response. This finding is similar to a study done by Hew et al.28 Conversely, Seldon et al. demonstrated that grade III tumors have a significantly higher likelihood of achieving a pathologic complete response when compared to grade II STS.21 Tumor depth did not impact levels of necrosis in this study. Again, this observation is contrary to that of Fromm et al. wherein deeper tumors were observed to have higher levels of necrosis.17 Surprisingly, our study found that female sex was associated with STS's that exhibited decreased tumor viability. Gender differences in patients with STS received little attention. Future studies should aim to clarify the relationships between tumor characteristics and pathologic complete response in soft tissue sarcoma. These investigations should employ robust methodologies to control for confounding factors, particularly the varying responses observed across different histopathological sarcoma subtypes. This approach will help isolate the true impact of each tumor characteristic on treatment response.
This study also performed a sub-analysis on patients that received no treatment prior to surgery to assess tumor based on tumor factors alone. Similar to our other cohort, size, grade, and depth had no impact on tissue necrosis. This finding further demonstrates that tumor type or other tumor specific factors may play a more significant role in determination of tumor necrosis than treatment. Although this subgroup of 60 neoadjuvant-naïve patients is relatively small, it provides valuable insights. Notably, the high rate of tumor necrosis observed in this group without preoperative treatment is particularly enlightening. This finding challenges the assumption that necrosis is primarily a result of neoadjuvant therapy. While the limited sample size necessitates cautious interpretation, these results underscore the need for larger studies controlling for neoadjuvant-naïve patients. Such research could elucidate the true impact of various treatments on tumor necrosis, as well as the intrinsic factors contributing to spontaneous necrosis in soft tissue sarcomas.
The current consensus is that utilization of radiotherapy reduces local recurrence in high grade soft tissue sarcoma.31,32 In this study, there was no difference in LR whether chemotherapy was added to standard radiation treatment or not. These findings are similar to the work of Hazell et al. that determined that neoadjuvant chemoradiation provides similar levels of local control compared to neoadjuvant radiation.33 This study also determined that tumor viability less than 10 % had no impact on LR on both univariate and multivariate analysis which is supported by similar findings in a study done by Fromm et al.17 This study also determined that positive margins, unplanned excisions, and fungating tumors were associated with an increased risk of LR, which is supported by previous studies.34, 35, 36 Similar results were found on sub analysis of neoadjuvant naive patients. In this group, addition of chemotherapy to traditional radiation therapy, tumor size, depth, and even grade (II or III) does not significantly impact local recurrence. This may be due to the relatively small sample size of 60 patients. However, the cohort did demonstrate that positive margins and an unplanned previous excision procedure statistically increase the risk of LR in these patients without neoadjuvant therapy. All efforts must be made for a planned excision with negative margins to minimize risk for recurrence. Additionally, patients with MFS that did not receive neoadjuvant treatment demonstrated a trend towards an increased risk of local recurrence compared to all other soft tissue sarcoma types, though this did not achieve significance with only 9 patients in our cohort diagnosed with MFS that did not receive neoadjuvant treatment. MFS has previously been described as an aggressive STS with high rate of LR compared to other subtypes, likely due to tail-like growth patterns and satellite cells which may increase risk of events such as positive margins and LR.37
This study of the relationship of tumor viability to oncologic outcomes has a number of limitations. The cohort includes only patients treated at a single sarcoma center, thereby limiting the generalizability of the outcomes to other institutions. The retrospective nature of the study only underscores associations, not causation. The study is limited by a small neoadjuvant naïve group and the results of this group should be assessed with caution. The study's small number limits the statistical power in considering more rare groups such as MFS, SCARE, and those treated without neoadjuvant treatment. Additionally, percent viability, assessed by even experienced pathologists, is subjective and prone to interpretation errors and furthermore, limits generalizability across institutions. Pre-treatment tumor viability was not evaluated due to the limitations of a core-needle biopsy and subjectivity of radiologic tumor necrosis values but may have provided valuable insight into the impact of neoadjuvant treatment on histopathologic tumor response. Finally, in this study, the impact of each tumor histological type, as well as variations and deviations in chemotherapy and radiotherapy regimens was not assessed and may impact the tumor specific outcomes studied.
This study evaluated the impact of tumor viability on metastasis and overall survival, but careful conclusions should be drawn from these results due rare occurrence of these events in the total cohort. This study found that tumor necrosis did not impact metastasis or overall survival on univariate analysis. This finding is supported by another study that found similar results in STS.17 This study also found that combined chemotherapy and radiation was associated with increased risk of metastasis on univariate analysis. An observation that is likely a product of selection bias and the retrospective nature of this investigation. This study highlights the need for collaborative databases to more effectively assess the impact of viability on oncologic outcomes with rare incidence, such as metastasis or mortality. Future studies should aim to determine more objective ways for evaluating tumor necrosis as a prognostic variable.
5. Conclusion
This study demonstrates that neoadjuvant therapies, size, grade, and tumor depth did not have a significant effect on tumor viability at the time of excision. Furthermore, patients with ≤10 % viability did not demonstrate a lower risk of LR, DM, and OS compared to patients with >10 % viability. More than 50 % of the neoadjuvant naive patients still had greater than 90 % necrosis, which raises the question of what the true impact of neoadjuvant therapy is on tissue necrosis. Other factors such as tumor type may play a more significant role in tumor necrosis than originally thought. Pathologic tissue response continues to guide management of STS, but these findings underscore the need for further investigations into tumor viability in soft tissue sarcoma, targeting specific treatments isolated in large collaborative studies.
CRediT authorship contribution statement
Julien Montreuil: Conceptualization, Methodology, Validation, Investigation, Writing – original draft, preparation, Writing – review & editing, Visualization, Supervision, Project administration. Eric Kholodovsky: Methodology, Validation, Investigation, Data curation, Writing – original draft, preparation, Writing – review & editing, Project administration. Moses Markowitz: Methodology, Validation, Investigation, Data curation, Writing – original draft, preparation, Writing – review & editing, Project administration. Sergio Torralbas Fitz: Formal analysis, Writing – review & editing. Dominic Campano: Writing – review & editing. J. Erik Geiger: Methodology, Validation, Investigation, Writing – review & editing. Francis Hornicek: Methodology, Validation, Investigation, Writing – review & editing. Brooke Crawford: Methodology, Validation, Investigation, Writing – review & editing. H. Thomas Temple: Conceptualization, Methodology, Validation, Investigation, Writing – review & editing, All authors have read and agreed to the published version of the manuscript.
Guardian/patient's consent
Informed consent was obtained for each patient as outlined in University of Miami IRB 20230560 for this Study.
Ethical statement
Ethical approval was obtained through the University of Miami IRB 20230560 for this Study.
Funding statement
The authors did not receive funding for this project.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
The Authors have no acknowledgements to make.
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